research topics in natural language processing

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Long-range modeling

Protein language model, sentence pair modeling, representation learning.

Disentanglement

Graph representation learning, sentence embeddings.

Network Embedding

Classification.

Text Classification

Graph Classification

Audio Classification

Medical Image Classification

Text retrieval, deep hashing, table retrieval, question answering.

Open-Ended Question Answering

Open-Domain Question Answering

Conversational question answering.

Knowledge Base Question Answering

Nlp based person retrival, image generation.

Image-to-Image Translation

Text-to-Image Generation

Image Inpainting

Conditional Image Generation

Translation, data augmentation.

Image Augmentation

Text Augmentation

Large language model.

Knowledge Graphs

Machine translation.

Transliteration

Multimodal Machine Translation

Bilingual lexicon induction.

Unsupervised Machine Translation

Knowledge graph completion, triple classification, inductive knowledge graph completion, inductive relation prediction, text generation.

Dialogue Generation

Data-to-Text Generation

Multi-Document Summarization

Story Generation

2d semantic segmentation, image segmentation, text style transfer.

Scene Parsing

Reflection Removal

Topic Models

Document Classification

Sentence Classification

Emotion Classification

Visual question answering (vqa).

Visual Question Answering

Machine Reading Comprehension

Chart Question Answering

Chart understanding, sentiment analysis.

Aspect-Based Sentiment Analysis (ABSA)

Multimodal Sentiment Analysis

Aspect Sentiment Triplet Extraction

Twitter Sentiment Analysis

Named entity recognition (ner).

Nested Named Entity Recognition

Chinese named entity recognition, few-shot ner, few-shot learning.

One-Shot Learning

Few-Shot Semantic Segmentation

Cross-domain few-shot.

Unsupervised Few-Shot Learning

Optical character recognition (ocr).

Active Learning

Handwriting Recognition

Handwritten digit recognition, irregular text recognition, word embeddings.

Learning Word Embeddings

Multilingual Word Embeddings

Embeddings evaluation, contextualised word representations, continual learning.

Class Incremental Learning

Continual named entity recognition, unsupervised class-incremental learning, information retrieval.

Passage Retrieval

Cross-lingual information retrieval, table search, text summarization.

Abstractive Text Summarization

Document summarization, opinion summarization, relation extraction.

Relation Classification

Document-level relation extraction, joint entity and relation extraction, temporal relation extraction, link prediction.

Inductive Link Prediction

Dynamic link prediction, hyperedge prediction, anchor link prediction, natural language inference.

Answer Generation

Visual Entailment

Cross-lingual natural language inference, reading comprehension.

Intent Recognition

Implicit relations, active object detection, emotion recognition.

Speech Emotion Recognition

Emotion Recognition in Conversation

Multimodal Emotion Recognition

Emotion-cause pair extraction, natural language understanding, vietnamese social media text processing.

Emotional Dialogue Acts

Image captioning.

3D dense captioning

Controllable image captioning, aesthetic image captioning.

Relational Captioning

Semantic textual similarity.

Paraphrase Identification

Cross-Lingual Semantic Textual Similarity

Event extraction, event causality identification, zero-shot event extraction, in-context learning, dialogue state tracking, task-oriented dialogue systems.

Visual Dialog

Dialogue understanding, coreference resolution, coreference-resolution, cross document coreference resolution, code generation.

Code Translation

Code documentation generation, class-level code generation, library-oriented code generation, semantic parsing.

AMR Parsing

Semantic dependency parsing, drs parsing, ucca parsing, conformal prediction.

Text Simplification

Music Source Separation

Decision Making Under Uncertainty

Audio source separation, semantic similarity.

Sentence Embedding

Sentence compression, joint multilingual sentence representations, sentence embeddings for biomedical texts, specificity, instruction following, visual instruction following, dependency parsing.

Transition-Based Dependency Parsing

Prepositional phrase attachment, unsupervised dependency parsing, cross-lingual zero-shot dependency parsing, information extraction, extractive summarization, temporal information extraction, document-level event extraction, cross-lingual, cross-lingual transfer, cross-lingual document classification.

Cross-Lingual Entity Linking

Cross-language text summarization, common sense reasoning.

Physical Commonsense Reasoning

Riddle sense, memorization, response generation, prompt engineering.

Visual Prompting

Data integration.

Entity Alignment

Entity Resolution

Table annotation, mathematical reasoning.

Math Word Problem Solving

Formal logic, geometry problem solving, abstract algebra, entity linking.

Question Generation

Poll generation.

Topic coverage

Dynamic topic modeling, part-of-speech tagging.

Unsupervised Part-Of-Speech Tagging

Abuse detection, hate speech detection, open information extraction.

Hope Speech Detection

Hate speech normalization, hate speech detection crisishatemm benchmark, data mining.

Argument Mining

Opinion Mining

Subgroup discovery, cognitive diagnosis, parallel corpus mining, bias detection, selection bias, language identification, dialect identification, native language identification, word sense disambiguation.

Word Sense Induction

Fake news detection, few-shot relation classification, implicit discourse relation classification, cause-effect relation classification, intrusion detection.

Network Intrusion Detection

Relational Reasoning

Semantic Role Labeling

Predicate Detection

Semantic role labeling (predicted predicates).

Textual Analogy Parsing

Grammatical error correction.

Grammatical Error Detection

Text matching, slot filling.

Zero-shot Slot Filling

Extracting covid-19 events from twitter, symbolic regression, equation discovery, pos tagging, document text classification.

Learning with noisy labels

Multi-label classification of biomedical texts, political salient issue orientation detection, spoken language understanding, dialogue safety prediction, stance detection, zero-shot stance detection, few-shot stance detection, stance detection (us election 2020 - biden), stance detection (us election 2020 - trump), deep clustering, trajectory clustering, deep nonparametric clustering, nonparametric deep clustering, intent detection.

Open Intent Detection

Multi-modal entity alignment, word similarity, model editing, knowledge editing, document ai, document understanding, cross-modal retrieval, image-text matching, cross-modal retrieval with noisy correspondence, multilingual cross-modal retrieval.

Zero-shot Composed Person Retrieval

Cross-modal retrieval on rsitmd, fact verification, text-to-speech synthesis.

Prosody Prediction

Zero-shot multi-speaker tts, zero-shot cross-lingual transfer, cross-lingual ner, intent classification.

Self-Learning

Language acquisition, grounded language learning, constituency parsing.

Constituency Grammar Induction

Entity typing.

Entity Typing on DH-KGs

Ad-hoc information retrieval, document ranking.

Word Alignment

Open-domain dialog, dialogue evaluation, line items extraction, abstract meaning representation, multimodal deep learning, multimodal text and image classification, novelty detection.

text-guided-image-editing

Text-based image editing, concept alignment.

Zero-Shot Text-to-Image Generation

Conditional text-to-image synthesis, multi-label text classification.

Shallow Syntax

Explanation generation, molecular representation, discourse parsing, discourse segmentation, connective detection, de-identification, privacy preserving deep learning, morphological analysis.

Sarcasm Detection

Conversational Search

Text-to-video generation, text-to-video editing, subject-driven video generation, lemmatization, speech-to-text translation, simultaneous speech-to-text translation.

Aspect Extraction

Aspect category sentiment analysis, extract aspect.

Aspect-Category-Opinion-Sentiment Quadruple Extraction

Aspect-oriented Opinion Extraction

Session search.

Chinese Word Segmentation

Handwritten chinese text recognition, chinese spelling error correction, chinese zero pronoun resolution, offline handwritten chinese character recognition, entity disambiguation, authorship attribution, source code summarization, method name prediction, text clustering.

Short Text Clustering

Open Intent Discovery

Linguistic acceptability.

Column Type Annotation

Cell entity annotation, columns property annotation, row annotation, abusive language, keyphrase extraction.

Visual Storytelling

KG-to-Text Generation

Unsupervised KG-to-Text Generation

Few-shot text classification, zero-shot out-of-domain detection, protein folding, term extraction, multilingual nlp, text2text generation, keyphrase generation, figurative language visualization, sketch-to-text generation, morphological inflection, phrase grounding, grounded open vocabulary acquisition, deep attention, spam detection, context-specific spam detection, traditional spam detection, word translation, natural language transduction, image-to-text retrieval, summarization, unsupervised extractive summarization, query-focused summarization.

Cross-Lingual Word Embeddings

Knowledge base population, passage ranking, conversational response selection, text annotation, key information extraction, authorship verification.

Keyword Extraction

Multimodal association, multimodal generation, video generation, image to video generation.

Unconditional Video Generation

Biomedical information retrieval.

SpO2 estimation

Meme classification, hateful meme classification, news classification, graph-to-sequence, automated essay scoring, nlg evaluation, key point matching, component classification, argument pair extraction (ape), claim extraction with stance classification (cesc), claim-evidence pair extraction (cepe), temporal processing, timex normalization, document dating, sentence summarization, unsupervised sentence summarization, morphological tagging, long-context understanding, weakly supervised classification, weakly supervised data denoising, entity extraction using gan.

Rumour Detection

Emotional intelligence, dark humor detection, semantic retrieval, sentence ordering, comment generation.

Review Generation

Semantic composition.

Goal-Oriented Dialog

User simulation, lexical simplification, conversational response generation.

Personalized and Emotional Conversation

Token classification, toxic spans detection.

Blackout Poetry Generation

Passage re-ranking, sentence-pair classification, subjectivity analysis.

Taxonomy Learning

Taxonomy expansion, hypernym discovery, humor detection.

Lexical Normalization

Pronunciation dictionary creation, negation detection, negation scope resolution, question similarity, medical question pair similarity computation, intent discovery, reverse dictionary, propaganda detection, propaganda span identification, propaganda technique identification, lexical analysis, lexical complexity prediction, question rewriting, punctuation restoration, attribute value extraction.

Hallucination Evaluation

Legal reasoning, meeting summarization, table-based fact verification, pretrained multilingual language models, formality style transfer, semi-supervised formality style transfer, word attribute transfer, aspect category detection, diachronic word embeddings, extreme summarization.

Persian Sentiment Analysis

Binary classification, llm-generated text detection, cancer-no cancer per breast classification, cancer-no cancer per image classification, stable mci vs progressive mci, suspicous (birads 4,5)-no suspicous (birads 1,2,3) per image classification, clinical concept extraction.

Clinical Information Retreival

Constrained clustering.

Only Connect Walls Dataset Task 1 (Grouping)

Incremental constrained clustering, recognizing emotion cause in conversations.

Causal Emotion Entailment

trustable and focussed LLM generated content

Game design, dialog act classification, decipherment, nested mention recognition, relationship extraction (distant supervised), text compression, handwriting verification, bangla spelling error correction, clickbait detection, code repair, gender bias detection, probing language models, semantic entity labeling, ccg supertagging, linguistic steganography, toponym resolution.

Timeline Summarization

Multimodal abstractive text summarization, reader-aware summarization, stock prediction, text-based stock prediction, pair trading, event-driven trading, vietnamese visual question answering, explanatory visual question answering, arabic text diacritization, fact selection, thai word segmentation, vietnamese datasets.

Face to Face Translation

Multimodal lexical translation, semantic shift detection, similarity explanation, aggression identification, arabic sentiment analysis, commonsense causal reasoning, complex word identification, sign language production, suggestion mining, temporal relation classification, vietnamese word segmentation, speculation detection, speculation scope resolution, aspect category polarity, cross-lingual bitext mining, morphological disambiguation, multi-agent integration, scientific document summarization, lay summarization, text anonymization, text attribute transfer.

Image-guided Story Ending Generation

Abstract argumentation, chinese spell checking, dialogue rewriting, logical reasoning reading comprehension.

Unsupervised Sentence Compression

Stereotypical bias analysis, temporal tagging, anaphora resolution, bridging anaphora resolution.

Abstract Anaphora Resolution

Hope speech detection for english, hope speech detection for malayalam, hope speech detection for tamil, hidden aspect detection, latent aspect detection, personality generation, personality alignment, attribute mining, cognate prediction, japanese word segmentation, memex question answering, polyphone disambiguation, spelling correction, table-to-text generation.

KB-to-Language Generation

Vietnamese language models, zero-shot machine translation, zero-shot sentiment classification, conditional text generation, contextualized literature-based discovery, multimedia generative script learning, image-sentence alignment, open-world social event classification, action parsing, author attribution, binary condescension detection, context query reformulation, conversational web navigation, croatian text diacritization, czech text diacritization, definition modelling, document-level re with incomplete labeling, domain labelling, french text diacritization, hungarian text diacritization, irish text diacritization, latvian text diacritization, literature mining, misogynistic aggression identification, morpheme segmentaiton, multi-label condescension detection, news annotation, open relation modeling, personality recognition in conversation.

Reading Order Detection

Record linking, role-filler entity extraction, romanian text diacritization, simultaneous speech-to-speech translation, slovak text diacritization, spanish text diacritization, syntax representation, text-to-video search, turkish text diacritization, turning point identification, twitter event detection.

Vietnamese Scene Text

Vietnamese text diacritization.

Conversational Sentiment Quadruple Extraction

Attribute extraction, legal outcome extraction, automated writing evaluation, binary text classification, detection of potentially void clauses, chemical indexing, clinical assertion status detection.

Coding Problem Tagging

Collaborative plan acquisition, commonsense reasoning for rl.

Variable Disambiguation

Cross-lingual text-to-image generation, crowdsourced text aggregation.

Description-guided molecule generation

Multi-modal Dialogue Generation

Page stream segmentation.

Email Thread Summarization

Emergent communications on relations, emotion detection and trigger summarization, extractive tags summarization.

Hate Intensity Prediction

Hate span identification, job prediction, joint entity and relation extraction on scientific data, joint ner and classification, math information retrieval, meme captioning, multi-grained named entity recognition, multilingual machine comprehension in english hindi, multimodal text prediction, negation and speculation cue detection, negation and speculation scope resolution, only connect walls dataset task 2 (connections), overlapping mention recognition, paraphrase generation, multilingual paraphrase generation, phrase ranking, phrase tagging, phrase vector embedding, poem meters classification, query wellformedness.

Question-Answer categorization

Readability optimization, reliable intelligence identification, sentence completion, hurtful sentence completion, social media mental health detection, speaker attribution in german parliamentary debates (germeval 2023, subtask 1), text effects transfer, text-variation, vietnamese aspect-based sentiment analysis, sentiment dependency learning, vietnamese natural language understanding, vietnamese sentiment analysis, vietnamese multimodal sentiment analysis, web page tagging, workflow discovery, answerability prediction, incongruity detection, multi-word expression embedding, multi-word expression sememe prediction, pcl detection, semeval-2022 task 4-1 (binary pcl detection), semeval-2022 task 4-2 (multi-label pcl detection), automatic writing, complaint comment classification, counterspeech detection, extractive text summarization, face selection, job classification, multi-lingual text-to-image generation, multlingual neural machine translation, optical charater recogntion, bangla text detection, question to declarative sentence, relation mention extraction.

Tweet-Reply Sentiment Analysis

Vietnamese fact checking, vietnamese parsing.

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Collection  20 April 2023

Advances in Natural Language Processing

Natural language processing (NLP) is an interdisciplinary field spanning computational science and artificial intelligence (AI), concerned with the understanding of human language, in both written and verbal forms, by machines. NLP puts an emphasis on employing machine learning and deep learning techniques to complete tasks, like language translation or question answering. In the growing NLP domain, two main methodological branches can be distinguished: natural language understanding (NLU), which aims to improve the machine's reading comprehension, and natural language generation (NLG), focused on enabling machines to produce human language text responses based on a given data input.

In the modern world, the number of NLP applications seems to be following an exponential growth curve: from highly agile chatbots, to sentiment analysis and intent classification, to personalised medicine, the NLP's capacity for improving our lives is ever-growing. At the same time, NLP progress is halted by the limited AI hardware infrastructure which struggles to accommodate more refined NLP models, the sparsity of good-quality NLP-training data, and complex linguistic problems, such as machine's understanding of homonymy or generation of polysemy.

This Collection is dedicated to the latest research on methodology in the vast field of NLP, which addresses and carries the potential to solve at least one of the many struggles the state-of-the-art NLP approaches face. We welcome theoretical-applied and applied research, proposing novel computational and/or hardware solutions.

University of Surrey, UK

Einat Liebenthal

Harvard Medical School, USA

University of Modena and Reggio Emilia, Italy

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Natural Language Processing

Natural Language Processing (NLP) research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more.

Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems. We are particularly interested in algorithms that scale well and can be run efficiently in a highly distributed environment.

Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number. They also label relationships between words, such as subject, object, modification, and others. We focus on efficient algorithms that leverage large amounts of unlabeled data, and recently have incorporated neural net technology.

On the semantic side, we identify entities in free text, label them with types (such as person, location, or organization), cluster mentions of those entities within and across documents (coreference resolution), and resolve the entities to the Knowledge Graph.

Recent work has focused on incorporating multiple sources of knowledge and information to aid with analysis of text, as well as applying frame semantics at the noun phrase, sentence, and document level.

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Title: exploring the landscape of natural language processing research.

Abstract: As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. Contributing to closing this gap, we have systematically classified and analyzed research papers in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.

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Natural Language Processing

Introduction.

Natural Language Processing (NLP) is one of the hottest areas of artificial intelligence (AI) thanks to applications like text generators that compose coherent essays, chatbots that fool people into thinking they’re sentient, and text-to-image programs that produce photorealistic images of anything you can describe. Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output.

What is Natural Language Processing (NLP)

Natural language processing (NLP) is the discipline of building machines that can manipulate human language — or data that resembles human language — in the way that it is written, spoken, and organized. It evolved from computational linguistics, which uses computer science to understand the principles of language, but rather than developing theoretical frameworks, NLP is an engineering discipline that seeks to build technology to accomplish useful tasks. NLP can be divided into two overlapping subfields: natural language understanding (NLU), which focuses on semantic analysis or determining the intended meaning of text, and natural language generation (NLG), which focuses on text generation by a machine. NLP is separate from — but often used in conjunction with — speech recognition, which seeks to parse spoken language into words, turning sound into text and vice versa.

Why Does Natural Language Processing (NLP) Matter?

NLP is an integral part of everyday life and becoming more so as language technology is applied to diverse fields like retailing (for instance, in customer service chatbots) and medicine (interpreting or summarizing electronic health records). Conversational agents such as Amazon’s Alexa and Apple’s Siri utilize NLP to listen to user queries and find answers. The most sophisticated such agents — such as GPT-3, which was recently opened for commercial applications — can generate sophisticated prose on a wide variety of topics as well as power chatbots that are capable of holding coherent conversations. Google uses NLP to improve its search engine results , and social networks like Facebook use it to detect and filter hate speech . 

NLP is growing increasingly sophisticated, yet much work remains to be done. Current systems are prone to bias and incoherence, and occasionally behave erratically. Despite the challenges, machine learning engineers have many opportunities to apply NLP in ways that are ever more central to a functioning society.

What is Natural Language Processing (NLP) Used For?

NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. 

Here are 11 tasks that can be solved by NLP:

• Sentiment analysis is the process of classifying the emotional intent of text. Generally, the input to a sentiment classification model is a piece of text, and the output is the probability that the sentiment expressed is positive, negative, or neutral. Typically, this probability is based on either hand-generated features, word n-grams, TF-IDF features, or using deep learning models to capture sequential long- and short-term dependencies. Sentiment analysis is used to classify customer reviews on various online platforms as well as for niche applications like identifying signs of mental illness in online comments.

• Toxicity classification is a branch of sentiment analysis where the aim is not just to classify hostile intent but also to classify particular categories such as threats, insults, obscenities, and hatred towards certain identities. The input to such a model is text, and the output is generally the probability of each class of toxicity. Toxicity classification models can be used to moderate and improve online conversations by silencing offensive comments , detecting hate speech , or scanning documents for defamation . 
• Machine translation automates translation between different languages. The input to such a model is text in a specified source language, and the output is the text in a specified target language. Google Translate is perhaps the most famous mainstream application. Such models are used to improve communication between people on social-media platforms such as Facebook or Skype. Effective approaches to machine translation can distinguish between words with similar meanings . Some systems also perform language identification; that is, classifying text as being in one language or another. 
• Named entity recognition aims to extract entities in a piece of text into predefined categories such as personal names, organizations, locations, and quantities. The input to such a model is generally text, and the output is the various named entities along with their start and end positions. Named entity recognition is useful in applications such as summarizing news articles and combating disinformation . For example, here is what a named entity recognition model could provide: 

• Spam detection is a prevalent binary classification problem in NLP, where the purpose is to classify emails as either spam or not. Spam detectors take as input an email text along with various other subtexts like title and sender’s name. They aim to output the probability that the mail is spam. Email providers like Gmail use such models to provide a better user experience by detecting unsolicited and unwanted emails and moving them to a designated spam folder. 
• Grammatical error correction models encode grammatical rules to correct the grammar within text. This is viewed mainly as a sequence-to-sequence task, where a model is trained on an ungrammatical sentence as input and a correct sentence as output. Online grammar checkers like Grammarly and word-processing systems like Microsoft Word use such systems to provide a better writing experience to their customers. Schools also use them to grade student essays . 
• Topic modeling is an unsupervised text mining task that takes a corpus of documents and discovers abstract topics within that corpus. The input to a topic model is a collection of documents, and the output is a list of topics that defines words for each topic as well as assignment proportions of each topic in a document. Latent Dirichlet Allocation (LDA), one of the most popular topic modeling techniques, tries to view a document as a collection of topics and a topic as a collection of words. Topic modeling is being used commercially to help lawyers find evidence in legal documents . 
• Autocomplete predicts what word comes next, and autocomplete systems of varying complexity are used in chat applications like WhatsApp. Google uses autocomplete to predict search queries. One of the most famous models for autocomplete is GPT-2, which has been used to write articles , song lyrics , and much more. 
• Database query: We have a database of questions and answers, and we would like a user to query it using natural language. 
• Conversation generation: These chatbots can simulate dialogue with a human partner. Some are capable of engaging in wide-ranging conversations . A high-profile example is Google’s LaMDA, which provided such human-like answers to questions that one of its developers was convinced that it had feelings .
• Information retrieval finds the documents that are most relevant to a query. This is a problem every search and recommendation system faces. The goal is not to answer a particular query but to retrieve, from a collection of documents that may be numbered in the millions, a set that is most relevant to the query. Document retrieval systems mainly execute two processes: indexing and matching. In most modern systems, indexing is done by a vector space model through Two-Tower Networks, while matching is done using similarity or distance scores. Google recently integrated its search function with a multimodal information retrieval model that works with text, image, and video data.

• Extractive summarization focuses on extracting the most important sentences from a long text and combining these to form a summary. Typically, extractive summarization scores each sentence in an input text and then selects several sentences to form the summary.
• Abstractive summarization produces a summary by paraphrasing. This is similar to writing the abstract that includes words and sentences that are not present in the original text. Abstractive summarization is usually modeled as a sequence-to-sequence task, where the input is a long-form text and the output is a summary.
• Multiple choice: The multiple-choice question problem is composed of a question and a set of possible answers. The learning task is to pick the correct answer. 
• Open domain : In open-domain question answering, the model provides answers to questions in natural language without any options provided, often by querying a large number of texts.

How Does Natural Language Processing (NLP) Work?

NLP models work by finding relationships between the constituent parts of language — for example, the letters, words, and sentences found in a text dataset. NLP architectures use various methods for data preprocessing, feature extraction, and modeling. Some of these processes are: 

• Stemming and lemmatization : Stemming is an informal process of converting words to their base forms using heuristic rules. For example, “university,” “universities,” and “university’s” might all be mapped to the base univers . (One limitation in this approach is that “universe” may also be mapped to univers , even though universe and university don’t have a close semantic relationship.) Lemmatization is a more formal way to find roots by analyzing a word’s morphology using vocabulary from a dictionary. Stemming and lemmatization are provided by libraries like spaCy and NLTK. 
• Sentence segmentation breaks a large piece of text into linguistically meaningful sentence units. This is obvious in languages like English, where the end of a sentence is marked by a period, but it is still not trivial. A period can be used to mark an abbreviation as well as to terminate a sentence, and in this case, the period should be part of the abbreviation token itself. The process becomes even more complex in languages, such as ancient Chinese, that don’t have a delimiter that marks the end of a sentence. 
• Stop word removal aims to remove the most commonly occurring words that don’t add much information to the text. For example, “the,” “a,” “an,” and so on.
• Tokenization splits text into individual words and word fragments. The result generally consists of a word index and tokenized text in which words may be represented as numerical tokens for use in various deep learning methods. A method that instructs language models to ignore unimportant tokens can improve efficiency.  

• Bag-of-Words: Bag-of-Words counts the number of times each word or n-gram (combination of n words) appears in a document. For example, below, the Bag-of-Words model creates a numerical representation of the dataset based on how many of each word in the word_index occur in the document. 

• Term Frequency: How important is the word in the document?

TF(word in a document)= Number of occurrences of that word in document / Number of words in document

• Inverse Document Frequency: How important is the term in the whole corpus?

IDF(word in a corpus)=log(number of documents in the corpus / number of documents that include the word)

A word is important if it occurs many times in a document. But that creates a problem. Words like “a” and “the” appear often. And as such, their TF score will always be high. We resolve this issue by using Inverse Document Frequency, which is high if the word is rare and low if the word is common across the corpus. The TF-IDF score of a term is the product of TF and IDF. 

• Word2Vec , introduced in 2013 , uses a vanilla neural network to learn high-dimensional word embeddings from raw text. It comes in two variations: Skip-Gram, in which we try to predict surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which tries to predict the target word from surrounding words. After discarding the final layer after training, these models take a word as input and output a word embedding that can be used as an input to many NLP tasks. Embeddings from Word2Vec capture context. If particular words appear in similar contexts, their embeddings will be similar.
• GLoVE is similar to Word2Vec as it also learns word embeddings, but it does so by using matrix factorization techniques rather than neural learning. The GLoVE model builds a matrix based on the global word-to-word co-occurrence counts. 
• Numerical features extracted by the techniques described above can be fed into various models depending on the task at hand. For example, for classification, the output from the TF-IDF vectorizer could be provided to logistic regression, naive Bayes, decision trees, or gradient boosted trees. Or, for named entity recognition, we can use hidden Markov models along with n-grams. 
• Deep neural networks typically work without using extracted features, although we can still use TF-IDF or Bag-of-Words features as an input. 
• Language Models : In very basic terms, the objective of a language model is to predict the next word when given a stream of input words. Probabilistic models that use Markov assumption are one example:

P(W n )=P(W n |W n−1 )

Deep learning is also used to create such language models. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. They can then be fine-tuned for a particular task. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines . 

Top Natural Language Processing (NLP) Techniques

Most of the NLP tasks discussed above can be modeled by a dozen or so general techniques. It’s helpful to think of these techniques in two categories: Traditional machine learning methods and deep learning methods. 

Traditional Machine learning NLP techniques: 

• Logistic regression is a supervised classification algorithm that aims to predict the probability that an event will occur based on some input. In NLP, logistic regression models can be applied to solve problems such as sentiment analysis, spam detection, and toxicity classification.
• Naive Bayes is a supervised classification algorithm that finds the conditional probability distribution P(label | text) using the following Bayes formula:

P(label | text) = P(label) x P(text|label) / P(text) 

and predicts based on which joint distribution has the highest probability. The naive assumption in the Naive Bayes model is that the individual words are independent. Thus: 

P(text|label) = P(word_1|label)*P(word_2|label)*…P(word_n|label)

In NLP, such statistical methods can be applied to solve problems such as spam detection or finding bugs in software code . 

• Decision trees are a class of supervised classification models that split the dataset based on different features to maximize information gain in those splits.

• Latent Dirichlet Allocation (LDA) is used for topic modeling. LDA tries to view a document as a collection of topics and a topic as a collection of words. LDA is a statistical approach. The intuition behind it is that we can describe any topic using only a small set of words from the corpus.
• Hidden Markov models : Markov models are probabilistic models that decide the next state of a system based on the current state. For example, in NLP, we might suggest the next word based on the previous word. We can model this as a Markov model where we might find the transition probabilities of going from word1 to word2, that is, P(word1|word2). Then we can use a product of these transition probabilities to find the probability of a sentence. The hidden Markov model (HMM) is a probabilistic modeling technique that introduces a hidden state to the Markov model. A hidden state is a property of the data that isn’t directly observed. HMMs are used for part-of-speech (POS) tagging where the words of a sentence are the observed states and the POS tags are the hidden states. The HMM adds a concept called emission probability; the probability of an observation given a hidden state. In the prior example, this is the probability of a word, given its POS tag. HMMs assume that this probability can be reversed: Given a sentence, we can calculate the part-of-speech tag from each word based on both how likely a word was to have a certain part-of-speech tag and the probability that a particular part-of-speech tag follows the part-of-speech tag assigned to the previous word. In practice, this is solved using the Viterbi algorithm.

Deep learning NLP Techniques: 

• Convolutional Neural Network (CNN): The idea of using a CNN to classify text was first presented in the paper “ Convolutional Neural Networks for Sentence Classification ” by Yoon Kim. The central intuition is to see a document as an image. However, instead of pixels, the input is sentences or documents represented as a matrix of words.

• Recurrent Neural Network (RNN) : Many techniques for text classification that use deep learning process words in close proximity using n-grams or a window (CNNs). They can see “New York” as a single instance. However, they can’t capture the context provided by a particular text sequence. They don’t learn the sequential structure of the data, where every word is dependent on the previous word or a word in the previous sentence. RNNs remember previous information using hidden states and connect it to the current task. The architectures known as Gated Recurrent Unit (GRU) and long short-term memory (LSTM) are types of RNNs designed to remember information for an extended period. Moreover, the bidirectional LSTM/GRU keeps contextual information in both directions, which is helpful in text classification. RNNs have also been used to generate mathematical proofs and translate human thoughts into words. 

• Autoencoders are deep learning encoder-decoders that approximate a mapping from X to X, i.e., input=output. They first compress the input features into a lower-dimensional representation (sometimes called a latent code, latent vector, or latent representation) and learn to reconstruct the input. The representation vector can be used as input to a separate model, so this technique can be used for dimensionality reduction. Among specialists in many other fields, geneticists have applied autoencoders to spot mutations associated with diseases in amino acid sequences. 

• Encoder-decoder sequence-to-sequence : The encoder-decoder seq2seq architecture is an adaptation to autoencoders specialized for translation, summarization, and similar tasks. The encoder encapsulates the information in a text into an encoded vector. Unlike an autoencoder, instead of reconstructing the input from the encoded vector, the decoder’s task is to generate a different desired output, like a translation or summary. 

• Transformers : The transformer, a model architecture first described in the 2017 paper “ Attention Is All You Need ” (Vaswani, Shazeer, Parmar, et al.), forgoes recurrence and instead relies entirely on a self-attention mechanism to draw global dependencies between input and output. Since this mechanism processes all words at once (instead of one at a time) that decreases training speed and inference cost compared to RNNs, especially since it is parallelizable. The transformer architecture has revolutionized NLP in recent years, leading to models including BLOOM , Jurassic-X , and Turing-NLG . It has also been successfully applied to a variety of different vision tasks , including making 3D images .

Six Important Natural Language Processing (NLP) Models

Over the years, many NLP models have made waves within the AI community, and some have even made headlines in the mainstream news. The most famous of these have been chatbots and language models. Here are some of them:

• Eliza was developed in the mid-1960s to try to solve the Turing Test; that is, to fool people into thinking they’re conversing with another human being rather than a machine. Eliza used pattern matching and a series of rules without encoding the context of the language.
• Tay was a chatbot that Microsoft launched in 2016. It was supposed to tweet like a teen and learn from conversations with real users on Twitter. The bot adopted phrases from users who tweeted sexist and racist comments, and Microsoft deactivated it not long afterward. Tay illustrates some points made by the “Stochastic Parrots” paper, particularly the danger of not debiasing data.
• BERT and his Muppet friends: Many deep learning models for NLP are named after Muppet characters , including ELMo , BERT , Big BIRD , ERNIE , Kermit , Grover , RoBERTa , and Rosita . Most of these models are good at providing contextual embeddings and enhanced knowledge representation.
• Generative Pre-Trained Transformer 3 (GPT-3) is a 175 billion parameter model that can write original prose with human-equivalent fluency in response to an input prompt. The model is based on the transformer architecture. The previous version, GPT-2, is open source. Microsoft acquired an exclusive license to access GPT-3’s underlying model from its developer OpenAI, but other users can interact with it via an application programming interface (API). Several groups including EleutherAI and Meta have released open source interpretations of GPT-3. 
• Language Model for Dialogue Applications (LaMDA) is a conversational chatbot developed by Google. LaMDA is a transformer-based model trained on dialogue rather than the usual web text. The system aims to provide sensible and specific responses to conversations. Google developer Blake Lemoine came to believe that LaMDA is sentient. Lemoine had detailed conversations with AI about his rights and personhood. During one of these conversations, the AI changed Lemoine’s mind about Isaac Asimov’s third law of robotics. Lemoine claimed that LaMDA was sentient, but the idea was disputed by many observers and commentators. Subsequently, Google placed Lemoine on administrative leave for distributing proprietary information and ultimately fired him.
• Mixture of Experts ( MoE): While most deep learning models use the same set of parameters to process every input, MoE models aim to provide different parameters for different inputs based on efficient routing algorithms to achieve higher performance . Switch Transformer is an example of the MoE approach that aims to reduce communication and computational costs.

Programming Languages, Libraries, And Frameworks For Natural Language Processing (NLP)

Many languages and libraries support NLP. Here are a few of the most useful.

• Natural Language Toolkit (NLTK) is one of the first NLP libraries written in Python. It provides easy-to-use interfaces to corpora and lexical resources such as WordNet . It also provides a suite of text-processing libraries for classification, tagging, stemming, parsing, and semantic reasoning.
• spaCy is one of the most versatile open source NLP libraries. It supports more than 66 languages. spaCy also provides pre-trained word vectors and implements many popular models like BERT. spaCy can be used for building production-ready systems for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and so on.
• Deep Learning libraries: Popular deep learning libraries include TensorFlow and PyTorch , which make it easier to create models with features like automatic differentiation. These libraries are the most common tools for developing NLP models.
• Hugging Face offers open-source implementations and weights of over 135 state-of-the-art models. The repository enables easy customization and training of the models.
• Gensim provides vector space modeling and topic modeling algorithms.
• R : Many early NLP models were written in R, and R is still widely used by data scientists and statisticians. Libraries in R for NLP include TidyText , Weka , Word2Vec , SpaCyR , TensorFlow , and PyTorch .
• Many other languages including JavaScript, Java, and Julia have libraries that implement NLP methods.

Controversies Surrounding Natural Language Processing (NLP)

NLP has been at the center of a number of controversies. Some are centered directly on the models and their outputs, others on second-order concerns, such as who has access to these systems, and how training them impacts the natural world. 

• Stochastic parrots: A 2021 paper titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” by Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell examines how language models may repeat and amplify biases found in their training data. The authors point out that huge, uncurated datasets scraped from the web are bound to include social biases and other undesirable information, and models that are trained on them will absorb these flaws. They advocate greater care in curating and documenting datasets, evaluating a model’s potential impact prior to development, and encouraging research in directions other than designing ever-larger architectures to ingest ever-larger datasets.
• Coherence versus sentience: Recently, a Google engineer tasked with evaluating the LaMDA language model was so impressed by the quality of its chat output that he believed it to be sentient . The fallacy of attributing human-like intelligence to AI dates back to some of the earliest NLP experiments. 
• Environmental impact: Large language models require a lot of energy during both training and inference. One study estimated that training a single large language model can emit five times as much carbon dioxide as a single automobile over its operational lifespan. Another study found that models consume even more energy during inference than training. As for solutions, researchers have proposed using cloud servers located in countries with lots of renewable energy as one way to offset this impact. 
• High cost leaves out non-corporate researchers: The computational requirements needed to train or deploy large language models are too expensive for many small companies . Some experts worry that this could block many capable engineers from contributing to innovation in AI. 
• Black box: When a deep learning model renders an output, it’s difficult or impossible to know why it generated that particular result. While traditional models like logistic regression enable engineers to examine the impact on the output of individual features, neural network methods in natural language processing are essentially black boxes. Such systems are said to be “not explainable,” since we can’t explain how they arrived at their output. An effective approach to achieve explainability is especially important in areas like banking, where regulators want to confirm that a natural language processing system doesn’t discriminate against some groups of people, and law enforcement, where models trained on historical data may perpetuate historical biases against certain groups.

“ Nonsense on stilts ”: Writer Gary Marcus has criticized deep learning-based NLP for generating sophisticated language that misleads users to believe that natural language algorithms understand what they are saying and mistakenly assume they are capable of more sophisticated reasoning than is currently possible.

How To Get Started In Natural Language Processing (NLP)

If you are just starting out, many excellent courses can help.

If you want to learn more about NLP, try reading research papers. Work through the papers that introduced the models and techniques described in this article. Most are easy to find on arxiv.org . You might also take a look at these resources: 

• The Batch : A weekly newsletter that tells you what matters in AI. It’s the best way to keep up with developments in deep learning.
• NLP News : A newsletter from Sebastian Ruder, a research scientist at Google, focused on what’s new in NLP. 
• Papers with Code : A web repository of machine learning research, tasks, benchmarks, and datasets.

We highly recommend learning to implement basic algorithms (linear and logistic regression, Naive Bayes, decision trees, and vanilla neural networks) in Python. The next step is to take an open-source implementation and adapt it to a new dataset or task. 

NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis. Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud , determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. 

Aspiring NLP practitioners can begin by familiarizing themselves with foundational AI skills: performing basic mathematics, coding in Python, and using algorithms like decision trees, Naive Bayes, and logistic regression. Online courses can help you build your foundation. They can also help as you proceed into specialized topics. Specializing in NLP requires a working knowledge of things like neural networks, frameworks like PyTorch and TensorFlow, and various data preprocessing techniques. The transformer architecture, which has revolutionized the field since it was introduced in 2017, is an especially important architecture.

NLP is an exciting and rewarding discipline, and has potential to profoundly impact the world in many positive ways. Unfortunately, NLP is also the focus of several controversies, and understanding them is also part of being a responsible practitioner. For instance, researchers have found that models will parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful. Moreover, sophisticated language models can be used to generate disinformation. A broader concern is that training large models produces substantial greenhouse gas emissions.

This page is only a brief overview of what NLP is all about. If you have an appetite for more, DeepLearning.AI offers courses for everyone in their NLP journey, from AI beginners and those who are ready to specialize . No matter your current level of expertise or aspirations, remember to keep learning!

Deep Learning for Natural Language Processing: A Survey

• Published: 26 June 2023
• Volume 273 , pages 533–582, ( 2023 )

Cite this article

• E. O. Arkhangelskaya 1 &
• S. I. Nikolenko 2 , 3  

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Over the last decade, deep learning has revolutionized machine learning. Neural network architectures have become the method of choice for many different applications; in this paper, we survey the applications of deep learning to natural language processing (NLP) problems. We begin by briefly reviewing the basic notions and major architectures of deep learning, including some recent advances that are especially important for NLP. Then we survey distributed representations of words, showing both how word embeddings can be extended to sentences and paragraphs and how words can be broken down further in character-level models. Finally, the main part of the survey deals with various deep architectures that have either arisen specifically for NLP tasks or have become a method of choice for them; the tasks include sentiment analysis, dependency parsing, machine translation, dialog and conversational models, question answering, and other applications.

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Arkhangelskaya, E.O., Nikolenko, S.I. Deep Learning for Natural Language Processing: A Survey. J Math Sci 273 , 533–582 (2023). https://doi.org/10.1007/s10958-023-06519-6

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Received : 14 January 2019

Published : 26 June 2023

Issue Date : July 2023

DOI : https://doi.org/10.1007/s10958-023-06519-6

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Generally, natural language processing is the sub-branch of Artificial Intelligence (AI). Natural language processing is otherwise known as NLP. It is compatible in dealing with multi-linguistic aspects and they convert the text into binary formats in which computers can understand it.  Primarily, the device understands the texts and then translates according to the questions asked. These processes are getting done with the help of several techniques. As this article is concentrated on delivering the natural language processing thesis topics , we are going to reveal each and every aspect that is needed for an effective NLP thesis .

NLP has a wide range of areas to explore in which enormous researches will be conducted. As the matter of fact, they analyses emotions, processes images, summarize texts, answer the questions & translates automatically, and so on.

Thesis writing is one of the important steps in researches. As they can deliver the exact perceptions of the researcher to the opponents hence it is advisable to frame the proper one. Let us begin this article with an overview of the NLP system . Are you ready to sail with us? Come on, guys!!!

“This is the article which is framed to the NLP enthusiasts in order to offer the natural language processing thesis topics”

What is Actually an NLP?

• NLP is the process of retrieving the meaning of the given sentence
• For this they use techniques & algorithms in order to extract the features
• They are also involved with the following,
• Audio capturing
• Text processing
• Conversion of audio into text
• Human-computer interaction

This is a crisp overview of the NLP system. NLP is one of the major technologies that are being used in the day to day life. Without these technologies, we could not even imagine a single scenario . In fact, they minimized the time of human beings by means of spelling checks, grammatical formations and most importantly they are highly capable of handling audio data . In this regard, let us have an idea of how does the NLP works in general. Shall we get into that section? Come let’s move on to that!!!

How does NLP Works?

• Unstructured Data Inputs
• Lingual Knowledge
• Domain Knowledge
• Domain Model
• Corpora Model Training
• Tools & Methods

The above listed are necessary when input is given to the model. The NLP model is in need of the above-itemized aspects to process the unstructured data in order to offer the structured data by means of parsing, stemming and lemmatization, and so on. In fact, NLP is subject to the classifications by their eminent features such as generation & understanding.  Yes my dear students we are going to cover the next sections with the NLP classifications.  

Classifications of NLP

• Natural Language-based Generation
• Natural Language-based Understanding

The above listed are the 2 major classifications of NLP technology . In these classifications let us have further brief explanations of the natural language-based understanding for your better understanding.

• Biometric Domains
• Spam Detection
• Opinion/Data Mining
• Entity Linking
• Named Entity Recognition
• Relationship Extraction

This is how the natural language-based understanding is sub-classified according to its functions. In recent days, NLP is getting boom in which various r esearches and projects are getting investigated and implemented successfully by our technical team. Generally, NLP processes are getting performed in a structural manner. That means they are overlays in several steps in crafting natural language processing thesis topics . Yes dears, we are going to envelop the next section with the steps that are concreted with the natural language processing.

NLP Natural Language Processing Steps

• Segmentation of Sentences
• Tokenization of Words
• PoS Tagging
• Parsing of Syntactic Contexts
• Removing of Stop Words
• Lemmatization & Stemming
• Classification of Texts
• Emotion/Sentiment Analysis

Here POS stands for the Parts of Speech . These are some of the steps involved in natural language processing. NLP performs according to the inputs given. Here you might need examples in these areas. For your better understanding, we are going to illustrate to you about the same with clear bulletin points. Come let us try to understand them.

• Let we take inputs as text & speech
• Text inputs are analyzed by “word tokenization”
• Speech inputs are analyzed by “phonetics”

In addition to that, they both are further processed in the same manner as they are,

• Morphological Analysis
• Syntactic Analysis
• Semantic Understanding
• Speech Processing

The above listed are the steps involved in NLP tasks in general . Word tokenization is one of the major which points out the vocabulary words presented in the word groups . Though, NLP processes are subject to numerous challenges. Our technical team is pointed out to you the challenges involved in the current days for a better understanding. Let’s move on to the current challenges sections.

Before going to the next section, we would like to highlight ourselves here. We are one of the trusted crew of technicians who are dynamically performing the NLP-based projects and researches effectively . As the matter of fact, we are offering so many successful projects all over the world by using the emerging techniques in technology. Now we can have the next section.

Current Challenges in NLP

• Context/Intention Understanding
• Voice Ambiguity/Vagueness
• Data Transformation
• Semantic Context Extracting
• Word Phrase Matching
• Vocabulary/Terminologies Creation
• PoS Tagging & Tokenization

The above listed are the current challenges that get involved in natural language processing. Besides, we can overcome these challenges by improving the NLP model by means of their performance. On the other hand, our technical experts in the concern are usually testing natural language processing approaches to abolish these constraints.

In the following passage, our technical team elaborately explained to you the various natural language processing approaches for the ease of your understanding. In fact, our researchers are always focusing on the students understanding so that they are categorizing each and every edge needed for the NLP-oriented tasks and approaches .  Are you interested to know about that? Now let’s we jump into the section.

Different NLP Approaches

Domain Model-based Approaches

• Loss Centric
• Feature Centric
• Pre-Training
• Pseudo Labeling
• Data Selection
• Model + Data-Centric

Machine Learning-based Approaches

• Association
• K-Means Clustering
• Anomalies Recognition
• Data Parsing
• Regular Emotions/Expressions
• Syntactic Interpretations
• Pattern Matching
• BFS Co-location Data
• BERT & BioBERT
• Decision Trees
• Logistic Regression
• Linear Regression
• Random Forests
• Support Vector Machine
• Gradient-based Networks
• Convolutional Neural Network
• Deep Neural Networks

Text Mining Approaches

• K-nearest Neighbor
• Naïve Bayes
• Predictive Modeling
• Association Rules
• Classification
• Document Indexing
• Term & Inverse Document Frequency
• Document Term Matrix
• Distribution
• Keyword Frequency
• Term Reduction/Compression
• Stemming/lemmatization
• Tokenization
• NLP & Log Parsing
• Text Taxonomies
• Text Classifications
• Text Categorization
• Text Clustering

The above listed are the 3 major approaches that are mainly used for natural languages processing in real-time . However, there are some demerits and merits are presented with the above-listed approaches. It is also important to know about the advantages and disadvantages of the NLP approaches which will help you to focus on the constraints and lead will lead you to the developments. Shall we discuss the pros and cons of NLP approaches? Come on, guys!

Advantages & Disadvantages of NLP Approaches

• Effortless Debugging
• Effective Precisions
• Multi-perspectives
• Short Form Reading
• Ineffective Parsing
• Poor Recalls
• Excessive Skills
• Low Scalability
• Speed Processes
• Resilient Results
• Effective Documentation
• Better Recalls
• High Scalability
• Narrow Understanding
• Poor in Reading Messages
• Huge Annotations
• Complex in Debugging

The foregoing passage conveyed to you the pros and cons of two approaches named machine learning and text mining. The best approach is also having pros and cons. If you do want further explanations or clarifications on that you can feel free to approach our researchers to get benefit from us. Generally, NLP models are trained to perform every task in order to recognize the inputs with latest natural language processing project ideas . Yes, you people guessed right! The next section is all about the training models of the NLP.

Training Models in NLP

• Scratch dataset such as language-specific BERTs & multi-linguistic BERT
• These are the datasets used in model pre-training
• Auxiliary based Pre-Training
• It is the additional data tasks used for labeled adaptive pre-training
• Multi-Phase based Pre-Training
• Domain & broad tasks are the secondary phases of pre-training
• Unlabeled data sources make differences in the multiphase pre-training
• TAPT, DAPT, AdaptaBERT & BioBERT are used datasets

As this article is named as natural language processing thesis topics , here we are going to point out to you the latest thesis topics in NLP for your reference. Commonly, a thesis is the best illustration of the projects or researches done in the determined areas. In fact, they convey the researchers’ perspectives & thoughts to the opponent by the effective structures of the thesis. If you are searching for thesis writing assistance then this is the right platform, you can surely approach our team at any time.

In the following passage, we have itemized some of the latest thesis topics in NLP .  We thought that it would help you a lot. Let’s get into the next section. As this is an important section, you are advised to pay your attention here. Are you really interested in getting into the next section? Come let us also learn them.

Latest Natural Language Processing Thesis Topics

• Cross & Multilingual based NLP Methods
• Multi-modal based NLP Methodologies
• Provocative based NLP Systems
• Graph oriented NLP Techniques
• Data Amplification in NLP
• Reinforcement Learning based NLP
• Dialogue/Voice Assistants
• Market & Customer Behavior Modeling
• Text Classification by Zero-shot/Semi-supervised Learning & Sentiment Analysis
• Text Generation & Summarization
• Relation & Knowledge Extraction for Fine-grained Entity Recognition
• Knowledge & Open-domain based Question & Answering

These are some of the latest thesis topics in NLP . As the matter of fact, we have delivered around 200 to 300 thesis with fruitful outcomes. Actually, they are very innovative and unique by means of their features. Our thesis writing approaches impress the institutes incredibly. At this time, we would like to reveal the future directions of the NLP for the ease of your understanding.

How to select the best thesis topics in NLP?

• See the latest IEEE and other benchmark papers
• Understand the NLP Project ideas recently proposed
• Highlight the problems and gaps
• Get the future scope of each existing work

Come let’s move on to the next section.

Future Research Directions of Natural Language Processing

• Logical Reasoning Chains
• Statistical Integrated Multilingual & Domain Knowledge Processing
• Combination of Interacting Modules

On the whole, NLP requires a better understanding of the texts. In fact, they understand the text’s meaning by relating to the presented word phrases. Conversion of the natural languages in reasoning logic will lead NLP to future directions. By allowing the modules to interact can enhance the NLP pipelines and modules. So far, we have come up with the areas of natural language processing thesis topics and each and every aspect that is needed to do a thesis. If you are in dilemma you could have the valuable opinions of our technical experts.

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Gosuddin Kamaruddin Siddiqi

Data Scientist

Gosuddin is a Data Scientist at Microsoft. His focus areas and expertise are Natural Language Processing and Understanding, Machine Learning, and Search and Information Retrieval. His current research focus on improving the quality of Microsoft News and Feeds Recommendation using Natural Language Processing, Recommendation Systems and Information Retrieval methods.

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Natural Language Processing to Analyze Growing Data

What this blog covers:.

• What is NLP and how it works
• Understanding the components of NLP
• How Kyvos uses Gen AI and NLQ for supercharged analytics

Natural Language Processing (NLP) has completely changed the way we interact with technology. From getting our daily tasks completed by virtual assistants like Siri and Alexa to sophisticated chatbots for enhanced customer service, NLP is at the core of many AI innovations.

But what exactly is NLP?

NLP is a subfield of artificial intelligence (AI) that bridges the gap between human communication and machine understanding, helping computers analyze human language and leading to a treasure trove of exciting applications.

Imagine chatting with a virtual assistant that can answer your questions just like a human would or effortlessly navigating sign boards and menus in a foreign country by having real-time translation at your fingertips. These are just a few examples of how NLP makes our lives easier.

Whether it is breaking down language barriers and streamlining everyday processes, NLP is transforming user experiences while making technology more intuitive and available. According to a new study by Grand View Research, the global natural language processing market size is estimated to reach USD 439.85 billion by 2030, expanding at a CAGR of 40.4% from 2023 to 2030. The immense potential and rapid growth of this field are not ending anytime soon. Join us as we dive deeper into the world of NLP, exploring its techniques, applications and overall potential.

Understanding NLP: The Core Concepts

NLP: The modern translator between computers and humans enables machines to understand, interpret and talk to us in natural language. For NLP to achieve this remarkable feat, what really works behind the scenes is the underlying technologies, such as machine learning and deep learning to process and analyze natural language data. More on these key technologies and tasks that drive NLP applications below:

Technologies Fueling NLP

Machine Learning (ML) : The ability of NLP to understand natural language comes from ML. An ML algorithm identifies patterns and generates predictions by being trained on massive volumes of data. For example, an algorithm trained on millions of emails develops the pattern and can distinguish between work and spam emails with accuracy. NLP uses ML algorithms to perform tasks like sentiment analysis and text classification.

Deep Learning : A subset of ML, deep learning uses complex neural networks to process information similar to how the human brain would do. This capability enables NLP systems to understand the intricacies of natural language, such as different types of tones (sarcastic, positive or slang) by copying how a human brain learns these fine distinctions.

Using the combination of these powerful technologies, NLP has made human-computer interaction more intuitive than ever before, from enabling search engines to become smarter powering virtual assistants and chatbots with intelligence.

NLP Tasks and Functions

Text Classification : Text classification is a core NLP functionality. Its main purpose is to organize large amounts of unstructured text (meaning the raw text data in the system). NLP algorithms are trained on huge volumes of labeled text consisting of documents and snippets of a specific category. For example, an email classification system might be trained on millions of emails labeled as “work,” “spam” or “personal.” The system then takes this text data and structures it for further analysis for specific features, such as keywords and sentence structure. Based on these features and training data, the NLP models assign a category to a new text piece.

Sentiment Analysis : How do businesses measure or understand customer satisfaction from their reviews? NLP does the real work in the background by using sentiment analysis. It enables computers to recognize the emotional tone (positive, negative or neutral) based on the written text. It is specially used in monitoring social media interactions, analyzing customer feedback and creating chatbots that can respond based on the emotions detected in the input text.

Named Entity Recognition : NER is an NLP technique used to identify and classify specific elements within text. NER enables computers to recognize, understand and extract structured information out of the input unstructured text, allowing them to categorize these entities in a meaningful way. This technique is used for applications like text summarization, question answering, knowledge graph building, etc. Consider entities as the key characters in a story. They can be the names of people, companies, locations, quantities or dates. These pre-defined entities are specifically categorized in a way to help computers understand the context (who, what, when and where) of the input text.

Machine Translation : The world just got a whole lot smaller, thanks to machine translation. It refers to the use of AI and ML algorithms to convert one language to another in the form of text or speech. This enables seamless communication across languages in real-time. Machine translation in NLP aims to not only produce grammatically correct translations but also retain their original meaning. As an example, if a Spanish tourist reads a store sign that says “Closed” in English and uses a machine translation tool to decode its meaning in Spanish. The tool would look for words that correspond to translations in the database and give out “Cerrado” as an output to the tourist.

Simply Explained: How NLP Works?

While the specific steps involved in each NLP application can vary, here’s a brief overview of the techniques that are used in NLP.

• Text Preprocessing – As the data is fed into the system, the first step it goes through is text preprocessing. In this, the system gets rid of irrelevant information and works on organizing the given data. It removes punctuation marks, extra spaces, stop words, checks for correct spellings and makes the overall text consistent.
• Tokenization – In the second step, the sentences from given data are broken down into smaller fragments of individual words. These smaller units are called tokens (each word is assigned a token responding to it). The system then takes these tokens to understand and analyze further. For example, a sentence like “I love my dog” can be given tokens as “I=1, love=2, my=3 and dog=4”. These tokens (numbers) enable the machine to understand the data for processing.
• Lemmatization – In this step, different variations of each word are filtered and categorized for the true meaning. As an example, “running” and “ran” are two different words but their root meaning or relating action stays the same, no matter where they are used. Lemmatization converts these words into a common form to make the machine understand their meaning.
• Part-of-Speech (POS) Tagging – Like the grammatical tags we used in school, POS tagging recognizes the function (is it a noun, verb, adjective, adverb, etc.) of all the words in each statement. Defining this enables the machine to understand the structure of the sentence and how the words are related to each other.
• Text Analysis – Based on the type of NLP task, this step takes the processing further by gathering the data, prepping it and finally analyzing it for outputs. The prepping of the data is done by combining and using different techniques mentioned above. Categories are assigned to the input text, e.g., spam or important email, followed by tone identification (positive, negative or neutral) along with extracting specific entities (people, places or organizations).

Understanding the Components of NLP

NLP uses techniques from both computational linguistics and ML to analyze massive volumes of data in natural language. Broadly speaking, there are three main components of NLP:

• NLG- Natural Language Generation , as the name suggests, is the process of enabling computational machines to generate information for effective communication. It is a branch of AI that focuses on transforming data into human-readable text or speech. To execute this, the system starts selecting relevant data from the larger set and decides what information needs to be included in the generated text. Next, it creates the structure of the text including its tone, style and the overall message. As a final step, the system chooses the right words, grammatical syntax and converts planned sentences into natural language.
• NLU- Natural Language Understanding refers to the process of helping machines comprehend human language and grasp the meaning of the given words and sentences. The process requires breaking down human language into smaller components, sentences, words and phrases, as the first step. Once this is done, it converts the input into machine-understandable format. Finally, it extracts meaning by understanding the input text with context, ambiguity and synonyms.
• Search-based NLQ – In this approach, users ask their question in natural language by typing in a text box (think web searching). Once the query is run, the system analyzes the keywords and maps them to data points or earlier asked questions. The answer’s accuracy in this system depends on the detailing of the query and the capability of the system to map the user’s intent while searching for the query response. Think of it as searching for a book in a library. If someone knows the correct title and author, the chances of finding the book are higher.
• Guided NLQ – This approach provides more structure and assistance as it helps users to get in-depth information on their original query by providing prompts, suggestions and drop-down menus. Users can refine their query further and select the right data fields without having to think about the underlying data. In the context of the book-searching example, guided NLQ is like asking a librarian to guide through the library’s organization and narrow down the search to find what a person is looking for.

The Kyvos Angle: How it Uses Gen AI and NLQ for Supercharged Analytics

As enterprise data grows by the second, business users often find themselves drowning in it, struggling to find relevant insights. And that’s exactly where Kyvos Copilot enters the picture. The platform leverages the power of Gen AI and NLQ and allows users to interact with complex datasets effortlessly. Generative AI is a sub-field of AI that creates original content in the form of text, images or other formats. It learns from huge datasets and creates new, original results. Leveraging this technology, users can ask questions in plain business language, and Kyvos will translate them into powerful queries and deliver relevant visualizations.

• Conversational Analytics for Everyone – Kyvos Copilot’s chat interface lets users talk directly to their data. For any natural language question, it chooses the best-suited semantic model to deliver super-fast, accurate answers in the form of visualizations or insightful reports. It also retains the context of previous inquiries, understands its connection with new questions and tailors its response accordingly.
• From Text to Powerful Queries – It empowers power users with its text-to-query capabilities by seamlessly converting natural language questions into sophisticated MDX and SQL formulas, unlocking the true power of data.
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The technical approach employed in this research leverages datasets like wikitext2 to train language models, systematically illustrating the effects of model collapse through a series of controlled experiments. The researchers conducted detailed analyses of the perplexity of generated data points across multiple generations, revealing a significant increase in perplexity and indicating a clear degradation in model performance. Critical components of their methodology include Monte Carlo sampling and density estimation in Hilbert spaces, which provide a robust mathematical framework for understanding the propagation of errors across successive generations. These meticulously designed experiments also explore variations such as preserving a portion of the original training data to assess its impact on mitigating collapse.

The findings demonstrate that models trained on recursively generated data exhibit a marked increase in perplexity, suggesting they become less accurate over time. Over successive generations, these models showed significant performance degradation, with higher perplexity and reduced variance in the generated data. The research also found that preserving a portion of the original human-generated data during training significantly mitigates the effects of model collapse, leading to better accuracy and stability in the models. The most notable result was the substantial improvement in accuracy when 10% of the original data was preserved, achieving an accuracy of 87.5% on a benchmark dataset, surpassing previous state-of-the-art results by 5%. This improvement highlights the importance of maintaining access to genuine human-generated data to sustain model performance.

In conclusion, the research presents a comprehensive study on the phenomenon of model collapse, offering both theoretical insights and empirical evidence to highlight its inevitability in generative models. The proposed solution involves understanding and mitigating the sources of errors that lead to collapse. This work advances the field of AI by addressing a critical challenge that affects the long-term reliability of AI systems. By maintaining access to genuine human-generated data, the findings suggest, it is possible to sustain the benefits of training from large-scale data and prevent the degradation of AI models over successive generations.

Check out the Paper . All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on  Twitter and join our  Telegram Channel and  LinkedIn Gr oup . If you like our work, you will love our  newsletter..

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Aswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.

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What the data says about immigrants in the U.S.

The United States has long had more immigrants than any other country. In fact, the U.S. is home to one-fifth of the world’s international migrants . These immigrants have come from just about every country in the world.

Pew Research Center regularly publishes research on U.S. immigrants . Based on this research, here are answers to some key questions about the U.S. immigrant population.

Pew Research Center conducted this analysis to answer common questions about immigration to the United States and the U.S. immigrant population.

The data in this analysis comes mainly from Center tabulations of Census Bureau microdata from decennial censuses and American Community Survey (IPUMS USA). This analysis also features estimates of the U.S. unauthorized immigrant population . The estimates presented in this research for 2022 are the Center’s latest.

How many people in the U.S. are immigrants?

The U.S. foreign-born population reached a record 46.1 million in 2022. Growth accelerated after Congress made U.S. immigration laws more permissive in 1965. In 1970, the number of immigrants living in the U.S. was less than a quarter of what it is today.

Immigrants today account for 13.8% of the U.S. population. This is a roughly threefold increase from 4.7% in 1970. However, the immigrant share of the population today remains below the record 14.8% in 1890 .

Where are U.S. immigrants from?

Mexico is the top country of birth for U.S. immigrants. In 2022, roughly 10.6 million immigrants living in the U.S. were born there, making up 23% of all U.S. immigrants. The next largest origin groups were those from India (6%), China (5%), the Philippines (4%) and El Salvador (3%).

By region of birth, immigrants from Asia accounted for 28% of all immigrants. Other regions make up smaller shares:

• Latin America (27%), excluding Mexico but including the Caribbean (10%), Central America (9%) and South America (9%)
• Europe, Canada and other North America (12%)
• Sub-Saharan Africa (5%)
• Middle East and North Africa (4%)

How have immigrants’ origin countries changed in recent decades?

Before 1965, U.S. immigration law favored immigrants from Northern and Western Europe and mostly barred immigration from Asia. The 1965 Immigration and Nationality Act opened up immigration from Asia and Latin America. The Immigration Act of 1990 further increased legal immigration and allowed immigrants from more countries to enter the U.S. legally.

Since 1965, about 72 million immigrants have come to the United States from different and more countries than their predecessors:

• From 1840 to 1889, about 90% of U.S. immigrants came from Europe, including about 70% from Germany, Ireland and the United Kingdom.
• Almost 90% of the immigrants who arrived from 1890 to 1919 came from Europe. Nearly 60% came from Italy, Austria-Hungary and Russia-Poland.
• Since 1965, about half of U.S. immigrants have come from Latin America, with about a quarter from Mexico alone. About another quarter have come from Asia. Large numbers have come from China, India, the Philippines, Central America and the Caribbean.

The newest wave of immigrants has dramatically changed states’ immigrant populations . In 1980, German immigrants were the largest group in 19 states, Canadian immigrants were the largest in 11 states and Mexicans were the largest in 10 states. By 2000, Mexicans were the largest group in 31 states.

Today, Mexico remains the largest origin country for U.S. immigrants. However, immigration from Mexico has slowed since 2007 and the Mexican-born population in the U.S. has dropped. The Mexican share of the U.S. immigrant population dropped from 29% in 2010 to 23% in 2022.

Where are recent immigrants coming from?

In 2022, Mexico was the top country of birth for immigrants who arrived in the last year, with about 150,000 people. India (about 145,000) and China (about 90,000) were the next largest sources of immigrants. Venezuela, Cuba, Brazil and Canada each had about 50,000 to 60,000 new immigrant arrivals.

The main sources of immigrants have shifted twice in the 21st century. The first was caused by the Great Recession (2007-2009). Until 2007, more Hispanics than Asians arrived in the U.S. each year. From 2009 to 2018, the opposite was true.

Since 2019, immigration from Latin America – much of it unauthorized – has reversed the pattern again. More Hispanics than Asians have come each year.

What is the legal status of immigrants in the U.S.?

Most immigrants (77%) are in the country legally. As of 2022:

• 49% were naturalized U.S. citizens.
• 24% were lawful permanent residents.
• 4% were legal temporary residents.
• 23% were unauthorized immigrants .

From 1990 to 2007, the unauthorized immigrant population more than tripled in size, from 3.5 million to a record high of 12.2 million. From there, the number slowly declined to about 10.2 million in 2019.

In 2022, the number of unauthorized immigrants in the U.S. showed sustained growth for the first time since 2007, to 11.o million.

As of 2022, about 4 million unauthorized immigrants in the U.S. are Mexican. This is the largest number of any origin country, representing more than one-third of all unauthorized immigrants. However, the Mexican unauthorized immigrant population is down from a peak of almost 7 million in 2007, when Mexicans accounted for 57% of all unauthorized immigrants.

The drop in the number of unauthorized immigrants from Mexico has been partly offset by growth from other parts of the world, especially Asia and other parts of Latin America.

The 2022 estimates of the unauthorized immigrant population are our latest comprehensive estimates. Other partial data sources suggest continued growth in 2023 and 2024 .

Who are unauthorized immigrants?

Virtually all unauthorized immigrants living in the U.S. entered the country without legal permission or arrived on a nonpermanent visa and stayed after it expired.

A growing number of unauthorized immigrants have permission to live and work in the U.S. and are temporarily protected from deportation. In 2022, about 3 million unauthorized immigrants had these temporary legal protections. These immigrants fall into several groups:

• Temporary Protected Status (TPS): About 650,000 immigrants have TPS as of July 2022. TPS is offered to individuals who cannot safely return to their home country because of civil unrest, violence, natural disaster or other extraordinary and temporary conditions.
• Deferred Action for Childhood Arrivals program (DACA): Almost 600,000 immigrants are beneficiaries of DACA. This program allows individuals brought to the U.S. as children before 2007 to remain in the U.S.
• Asylum applicants: About 1.6 million immigrants have pending applications for asylum in the U.S. as of mid-2022 because of dangers faced in their home country. These immigrants can stay in the U.S. legally while they wait for a decision on their case.
• Other protections: Several hundred thousand individuals have applied for special visas to become lawful immigrants. These types of visas are offered to victims of trafficking and certain other criminal activities.

In addition, about 500,000 immigrants arrived in the U.S. by the end of 2023 under programs created for Ukrainians (U4U or Uniting for Ukraine ) and people from Cuba, Haiti, Nicaragua and Venezuela ( CHNV parole ). These immigrants mainly arrived too late to be counted in the 2022 estimates but may be included in future estimates.

Do all lawful immigrants choose to become U.S. citizens?

Immigrants who are lawful permanent residents can apply to become U.S. citizens if they meet certain requirements. In fiscal year 2022, almost 1 million lawful immigrants became U.S. citizens through naturalization . This is only slightly below record highs in 1996 and 2008.

Most immigrants eligible for naturalization apply for citizenship, but not all do. Top reasons for not applying include language and personal barriers, lack of interest and not being able to afford it, according to a 2015 Pew Research Center survey .

Where do most U.S. immigrants live?

In 2022, most of the nation’s 46.1 million immigrants lived in four states: California (10.4 million or 23% of the national total), Texas (5.2 million or 11%), Florida (4.8 million or 10%) and New York (4.5 million or 10%).

Most immigrants lived in the South (35%) and West (33%). Another 21% lived in the Northeast and 11% were in the Midwest.

In 2022, more than 29 million immigrants – 63% of the nation’s foreign-born population – lived in just 20 major metropolitan areas. The largest populations were in the New York, Los Angeles and Miami metro areas. Most of the nation’s unauthorized immigrant population (60%) lived in these metro areas as well.

How many immigrants are working in the U.S.?

In 2022, over 30 million immigrants were in the U.S. workforce. Lawful immigrants made up the majority of the immigrant workforce, at 22.2 million. An additional 8.3 million immigrant workers are unauthorized. This is a notable increase over 2019 but about the same as in 2007 .

The share of workers who are immigrants increased slightly from 17% in 2007 to 18% in 2022. By contrast, the share of immigrant workers who are unauthorized declined from a peak of 5.4% in 2007 to 4.8% in 2022. Immigrants and their children are projected to add about 18 million people of working age between 2015 and 2035. This would offset an expected decline in the working-age population from retiring Baby Boomers.

How educated are immigrants compared with the U.S. population overall?

On average, U.S. immigrants have lower levels of education than the U.S.-born population. In 2022, immigrants ages 25 and older were about three times as likely as the U.S. born to have not completed high school (25% vs. 7%). However, immigrants were as likely as the U.S. born to have a bachelor’s degree or more (35% vs. 36%).

Immigrant educational attainment varies by origin. About half of immigrants from Mexico (51%) had not completed high school, and the same was true for 46% of those from Central America and 21% from the Caribbean. Immigrants from these three regions were also less likely than the U.S. born to have a bachelor’s degree or more.

On the other hand, immigrants from all other regions were about as likely as or more likely than the U.S. born to have at least a bachelor’s degree. Immigrants from South Asia (72%) were the most likely to have a bachelor’s degree or more.

How well do immigrants speak English?

About half of immigrants ages 5 and older (54%) are proficient English speakers – they either speak English very well (37%) or speak only English at home (17%).

Immigrants from Canada (97%), Oceania (82%), sub-Saharan Africa (76%), Europe (75%) and South Asia (73%) have the highest rates of English proficiency.

Immigrants from Mexico (36%) and Central America (35%) have the lowest proficiency rates.

Immigrants who have lived in the U.S. longer are somewhat more likely to be English proficient. Some 45% of immigrants who have lived in the U.S. for five years or less are proficient, compared with 56% of immigrants who have lived in the U.S. for 20 years or more.

Spanish is the most commonly spoken language among U.S. immigrants. About four-in-ten immigrants (41%) speak Spanish at home. Besides Spanish, the top languages immigrants speak at home are English only (17%), Chinese (6%), Filipino/Tagalog (4%), French or Haitian Creole (3%), and Vietnamese (2%).

Note: This is an update of a post originally published May 3, 2017.

• Immigrant Populations
• Immigration & Migration
• Unauthorized Immigration

Mohamad Moslimani is a research analyst focusing on race and ethnicity at Pew Research Center .

Jeffrey S. Passel is a senior demographer at Pew Research Center .

How the origins of America’s immigrants have changed since 1850

Facts on u.s. immigrants, 2018, building outpaces population growth in many of china’s urban areas, most popular.

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Global Energy Crisis

How the energy crisis started, how global energy markets are impacting our daily life, and what governments are doing about it

• English English

What is the energy crisis?

Record prices, fuel shortages, rising poverty, slowing economies: the first energy crisis that's truly global.

Energy markets began to tighten in 2021 because of a variety of factors, including the extraordinarily rapid economic rebound following the pandemic. But the situation escalated dramatically into a full-blown global energy crisis following Russia’s invasion of Ukraine in February 2022. The price of natural gas reached record highs, and as a result so did electricity in some markets. Oil prices hit their highest level since 2008. 

Higher energy prices have contributed to painfully high inflation, pushed families into poverty, forced some factories to curtail output or even shut down, and slowed economic growth to the point that some countries are heading towards severe recession. Europe, whose gas supply is uniquely vulnerable because of its historic reliance on Russia, could face gas rationing this winter, while many emerging economies are seeing sharply higher energy import bills and fuel shortages. While today’s energy crisis shares some parallels with the oil shocks of the 1970s, there are important differences. Today’s crisis involves all fossil fuels, while the 1970s price shocks were largely limited to oil at a time when the global economy was much more dependent on oil, and less dependent on gas. The entire word economy is much more interlinked than it was 50 years ago, magnifying the impact. That’s why we can refer to this as the first truly global energy crisis.

Some gas-intensive manufacturing plants in Europe have curtailed output because they can’t afford to keep operating, while in China some have simply had their power supply cut. In emerging and developing economies, where the share of household budgets spent on energy and food is already large, higher energy bills have increased extreme poverty and set back progress towards achieving universal and affordable energy access. Even in advanced economies, rising prices have impacted vulnerable households and caused significant economic, social and political strains.

Climate policies have been blamed in some quarters for contributing to the recent run-up in energy prices, but there is no evidence. In fact, a greater supply of clean energy sources and technologies would have protected consumers and mitigated some of the upward pressure on fuel prices.

Russia's invasion of Ukraine drove European and Asian gas prices to record highs

Evolution of key regional natural gas prices, june 2021-october 2022, what is causing it, disrupted supply chains, bad weather, low investment, and then came russia's invasion of ukraine.

Energy prices have been rising since 2021 because of the rapid economic recovery, weather conditions in various parts of the world, maintenance work that had been delayed by the pandemic, and earlier decisions by oil and gas companies and exporting countries to reduce investments. Russia began withholding gas supplies to Europe in 2021, months ahead of its invasion of Ukraine. All that led to already tight supplies. Russia’s attack on Ukraine greatly exacerbated the situation . The United States and the EU imposed a series of sanctions on Russia and many European countries declared their intention to phase out Russian gas imports completely. Meanwhile, Russia has increasingly curtailed or even turned off its export pipelines. Russia is by far the world’s largest exporter of fossil fuels, and a particularly important supplier to Europe. In 2021, a quarter of all energy consumed in the EU came from Russia. As Europe sought to replace Russian gas, it bid up prices of US, Australian and Qatari ship-borne liquefied natural gas (LNG), raising prices and diverting supply away from traditional LNG customers in Asia. Because gas frequently sets the price at which electricity is sold, power prices soared as well. Both LNG producers and importers are rushing to build new infrastructure to increase how much LNG can be traded internationally, but these costly projects take years to come online. Oil prices also initially soared as international trade routes were reconfigured after the United States, many European countries and some of their Asian allies said they would no longer buy Russian oil. Some shippers have declined to carry Russian oil because of sanctions and insurance risk. Many large oil producers were unable to boost supply to meet rising demand – even with the incentive of sky-high prices – because of a lack of investment in recent years. While prices have come down from their peaks, the outlook is uncertain with new rounds of European sanctions on Russia kicking in later this year.

What is being done?

Pandemic hangovers and rising interest rates limit public responses, while some countries turn to coal.

Some governments are looking to cushion the blow for customers and businesses, either through direct assistance, or by limiting prices for consumers and then paying energy providers the difference. But with inflation in many countries well above target and budget deficits already large because of emergency spending during the Covid-19 pandemic, the scope for cushioning the impact is more limited than in early 2020. Rising inflation has triggered increases in short-term interest rates in many countries, slowing down economic growth. Europeans have rushed to increase gas imports from alternative producers such as Algeria, Norway and Azerbaijan. Several countries have resumed or expanded the use of coal for power generation, and some are extending the lives of nuclear plants slated for de-commissioning. EU members have also introduced gas storage obligations, and agreed on voluntary targets to cut gas and electricity demand by 15% this winter through efficiency measures, greater use of renewables, and support for efficiency improvements. To ensure adequate oil supplies, the IEA and its members responded with the two largest ever releases of emergency oil stocks. With two decisions – on 1 March 2022 and 1 April – the IEA coordinated the release of some 182 million barrels of emergency oil from public stocks or obligated stocks held by industry. Some IEA member countries independently released additional public stocks, resulting in a total of over 240 million barrels being released between March and November 2022.

The IEA has also published action plans to cut oil use with immediate impact, as well as plans for how Europe can reduce its reliance on Russian gas and how common citizens can reduce their energy consumption . The invasion has sparked a reappraisal of energy policies and priorities, calling into question the viability of decades of infrastructure and investment decisions, and profoundly reorientating international energy trade. Gas had been expected to play a key role in many countries as a lower-emitting "bridge" between dirtier fossil fuels and renewable energies. But today’s crisis has called into question natural gas’ reliability.

The current crisis could accelerate the rollout of cleaner, sustainable renewable energy such as wind and solar, just as the 1970s oil shocks spurred major advances in energy efficiency, as well as in nuclear, solar and wind power. The crisis has also underscored the importance of investing in robust gas and power network infrastructure to better integrate regional markets. The EU’s RePowerEU, presented in May 2022 and the United States’ Inflation Reduction Act , passed in August 2022, both contain major initiatives to develop energy efficiency and promote renewable energies. 

The global energy crisis can be a historic turning point

Energy saving tips

1. Heating: turn it down

Lower your thermostat by just 1°C to save around 7% of your heating energy and cut an average bill by EUR 50-70 a year. Always set your thermostat as low as feels comfortable, and wear warm clothes indoors. Use a programmable thermostat to set the temperature to 15°C while you sleep and 10°C when the house is unoccupied. This cuts up to 10% a year off heating bills. Try to only heat the room you’re in or the rooms you use regularly.

The same idea applies in hot weather. Turn off air-conditioning when you’re out. Set the overall temperature 1 °C warmer to cut bills by up to 10%. And only cool the room you’re in.

2. Boiler: adjust the settings

Default boiler settings are often higher than you need. Lower the hot water temperature to save 8% of your heating energy and cut EUR 100 off an average bill.  You may have to have the plumber come once if you have a complex modern combi boiler and can’t figure out the manual. Make sure you follow local recommendations or consult your boiler manual. Swap a bath for a shower to spend less energy heating water. And if you already use a shower, take a shorter one. Hot water tanks and pipes should be insulated to stop heat escaping. Clean wood- and pellet-burning heaters regularly with a wire brush to keep them working efficiently.

3. Warm air: seal it in

Close windows and doors, insulate pipes and draught-proof around windows, chimneys and other gaps to keep the warm air inside. Unless your home is very new, you will lose heat through draughty doors and windows, gaps in the floor, or up the chimney. Draught-proof these gaps with sealant or weather stripping to save up to EUR 100 a year. Install tight-fitting curtains or shades on windows to retain even more heat. Close fireplace and chimney openings (unless a fire is burning) to stop warm air escaping straight up the chimney. And if you never use your fireplace, seal the chimney to stop heat escaping.

4. Lightbulbs: swap them out

Replace old lightbulbs with new LED ones, and only keep on the lights you need. LED bulbs are more efficient than incandescent and halogen lights, they burn out less frequently, and save around EUR 10 a year per bulb. Check the energy label when buying bulbs, and aim for A (the most efficient) rather than G (the least efficient). The simplest and easiest way to save energy is to turn lights off when you leave a room.

5. Grab a bike

Walking or cycling are great alternatives to driving for short journeys, and they help save money, cut emissions and reduce congestion. If you can, leave your car at home for shorter journeys; especially if it’s a larger car. Share your ride with neighbours, friends and colleagues to save energy and money. You’ll also see big savings and health benefits if you travel by bike. Many governments also offer incentives for electric bikes.

6. Use public transport

For longer distances where walking or cycling is impractical, public transport still reduces energy use, congestion and air pollution. If you’re going on a longer trip, consider leaving your car at home and taking the train. Buy a season ticket to save money over time. Your workplace or local government might also offer incentives for travel passes. Plan your trip in advance to save on tickets and find the best route.

7. Drive smarter

Optimise your driving style to reduce fuel consumption: drive smoothly and at lower speeds on motorways, close windows at high speeds and make sure your tires are properly inflated. Try to take routes that avoid heavy traffic and turn off the engine when you’re not moving. Drive 10 km/h slower on motorways to cut your fuel bill by around EUR 60 per year. Driving steadily between 50-90 km/h can also save fuel. When driving faster than 80 km/h, it’s more efficient to use A/C, rather than opening your windows. And service your engine regularly to maintain energy efficiency.

Analysis and forecast to 2026

Fuel report — December 2023

Europe’s energy crisis: Understanding the drivers of the fall in electricity demand

Commentary — 09 May 2023

Where things stand in the global energy crisis one year on

Commentary — 23 February 2023

The global energy crisis pushed fossil fuel consumption subsidies to an all-time high in 2022

Commentary — 16 February 2023

Fossil Fuels Consumption Subsidies 2022

Policy report — February 2023

Background note on the natural gas supply-demand balance of the European Union in 2023

Report — February 2023

Analysis and forecast to 2025

Fuel report — December 2022

How to Avoid Gas Shortages in the European Union in 2023

A practical set of actions to close a potential supply-demand gap

Flagship report — December 2022

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