Common Glossary Terminology

• ALBERT: A lite version of BERT with better performance on various NLP tasks.
• ALBERT: a transformer-based language model trained using a technique called factorization, which reduces the number of parameters and improves the performance on a wide range of natural language processing tasks.
• Anaphora Resolution: a specific type of coreference resolution that deals with pronouns and other anaphoric expressions.
• Anaphora Resolution: determining the antecedent of a pronoun or a noun phrase in a text.
• Attention Mechanism: a mechanism used in deep learning models to allow the model to focus on specific parts of the input when making predictions. Attention mechanisms are commonly used in transformer-based models such as BERT and GPT.
• Automatic Summarization: the task of creating a shorter version of a text that preserves the most important information.
• Bag-of-Words: a representation of a text where the order of the words is not considered and only the frequency of the words is taken into account.
• BERT: a pre-trained transformer-based neural network model for natural language processing tasks such as question answering and language inference.
• BERT: a transformer-based deep learning model for natural language processing tasks such as named entity recognition, question answering, and text classification. BERT stands for “Bidirectional Encoder Representations from Transformers.”
• BERT: a transformer-based language model that can be fine-tuned for a wide range of natural language processing tasks, such as question answering, text classification, and named entity recognition.
• Bullet Point List All Glossary Terminology And Related Definitions.
• Chatbot: a computer program designed to simulate conversation with human users.
• Coherence: the extent to which a text is logical and easy to understand.
• Cohesion: the extent to which different parts of a text are connected and related to each other.
• Computational Lexicography: the use of computational methods to create, analyze, and utilize dictionaries and other lexical resources.
• Constituency Parsing: the process of analyzing the grammatical structure of a sentence to determine the syntactic constituents, or phrases, that make up the sentence, such as noun phrases and verb phrases.
• Constituency Parsing: the task of analyzing the grammatical structure of a sentence by identifying its constituents or phrases.
• Constituency parsing: the task of analyzing the grammatical structure of a sentence, and representing it as a tree of constituents.
• Constituency Parsing: the task of analyzing the syntactic structure of a sentence and identifying the different constituents, such as noun phrases and verb phrases, that make up the sentence.
• Contextual Embeddings: a technique for representing words in a high-dimensional vector space, where the vector representation of a word is dependent on the context in which it appears.
• Co-occurrence: the measure of the association between two words, typically represented as the number of times they appear together in a text or corpus.
• Coreference Resolution: the process of identifying and linking mentions of the same entity across a text, such as “he” referring to “John Smith” earlier in the text.
• Coreference Resolution: identifying and linking mentions of the same entity or concept in a piece of text.
• Co-reference Resolution: the process of identifying and linking mentions of the same entity or concept in a piece of text.
• Coreference Resolution: determining when two or more expressions in a text refer to the same entity.
• Co-reference Resolution: the task of determining when two or more expressions in a text refer to the same entity.
• Coreference Resolution: identifying and linking mentions of the same entity in text.
• Coreference Resolution: the task of identifying when different words in a text refer to the same entity or concept.
• Coreference Resolution: identifying when different words in a text refer to the same entity or person.
• Coreference Resolution: the task of identifying when different words in a text refer to the same entity, such as when “he” refers to “John” in a text.
• Coreference Resolution: identifying when multiple expressions in a text refer to the same entity or object.
• Coreference Resolution: the task of identifying when two or more expressions in a text refer to the same entity or concept.
• Coreference Resolution: identifying when two or more expressions in a text refer to the same entity.
• Coreference Resolution: the task of identifying which words or phrases in a text refer to the same entity or person.
• CTRL: Conditional Transformer Language Model, a pre-trained model for generating text conditioned on a given topic or style.
• Deep Parsing: the process of analyzing a sentence to identify the syntactic structure at a deeper level, such as syntactic dependencies or constituency trees.
• Dependency Parsing: a type of parsing that analyzes the relationships between words in a sentence, such as subject-verb-object, to determine the sentence’s grammatical structure.
• Dependency Parsing: the process of analyzing the grammatical structure of a sentence and identifying the relationships between the words, such as subject, object, and modifier.
• Dependency Parsing: analyzing the grammatical structure of a sentence by identifying the dependencies between its words.
• Dependency Parsing: the process of analyzing the grammatical structure of a sentence to determine the relationships between words, such as subject-verb-object relationships.
• Dependency Parsing: the task of analyzing the grammatical relationships between words in a sentence, represented as a directed graph called a dependency tree.
• Dependency Parsing: the task of analyzing the grammatical relationships between words in a sentence, such as subject-verb relationships, by creating a dependency tree.
• Dependency Parsing: the task of analyzing the grammatical structure of a sentence by identifying the dependencies between its words.
• Dependency Parsing: the task of analyzing the grammatical structure of a sentence, and identifying the relationships between words, such as subject, verb, and object.
• Dependency parsing: the task of analyzing the grammatical structure of a sentence, and representing it as a graph of dependencies between words.
• Dialog Systems: systems that can understand and generate human-like text or speech for the purpose of carrying out a conversation with a user.
• Dialogue Evaluation: the process of evaluating the quality and fluency of a dialogue generated by a machine.
• Dialogue Generation: the process of generating a dialogue or conversation between multiple entities.
• Dialogue Generation: the process of generating responses in a conversation, using rule-based or machine learning methods.
• Dialogue Generation: the task of creating natural and coherent responses in a dialogue setting, often used in chatbots and virtual assistants.
• Dialogue Generation: the task of generating appropriate responses in a conversation, based on the context of the conversation and the user’s input.
• Dialogue Generation: the task of generating appropriate responses in a dialogue system, such as a chatbot or a virtual assistant.
• Dialogue Generation: the task of generating coherent and appropriate responses in a conversation.
• Dialogue Generation: the task of generating coherent and contextually appropriate responses in a conversational setting.
• Dialogue Generation: the task of generating human-like responses in a conversation, using context, knowledge and other information.
• Dialogue Management: the process of managing a conversation between a human and a machine, such as determining the next action or response based on previous inputs and context.
• Dialogue Management: the task of controlling the flow of a conversation and determining the appropriate response in a conversational setting.
• Dialogue Management: the task of coordinating the different components of a dialogue system and deciding what actions to take next.
• Dialogue State Tracking: the process of keeping track of the state of a conversation, such as the topic and the goals of the conversation.
• Dialogue State Tracking: the task of keeping track of the information exchanged in a dialogue and the goals of the user and the system.
• Dialogue Systems: a computer system that can engage in conversation with human users, also known as chatbots.
• Dialogue Systems: a system that can understand and generate natural language in order to have a conversation with humans.
• Dialogue Systems: systems that are able to engage in natural language dialogue with humans, such as chatbots or virtual assistants.
• Dialogue Systems: systems that can understand and generate natural language, and are designed to interact with humans through text or speech.
• Dialogue Systems: the task of creating computer systems that can engage in natural language conversations with humans.
• Discourse Analysis: the process of analyzing the structure and meaning of language in use within a certain context or discourse.
• Discourse Analysis: the study of how language is used in a broader conversational or discourse context, focusing on the organization and coherence of text or talk.
• Discourse Analysis: the study of how meaning is constructed across multiple sentences or texts, often used to analyze the coherence and cohesiveness of a text.
• Discourse Analysis: the study of language use in a social context, including the structure and organization of a piece of text.
• Discourse Analysis: the study of language use in texts, including the ways in which language is used to express meaning, and how meaning is constructed through text.
• Discourse Analysis: the study of the ways in which language is used in text and conversation, including how sentences and utterances relate to each other in a text.
• Discourse Analysis: the study of the ways in which language is used in texts and contexts, and the relations between language and context.
• Discourse Analysis: the study of the ways in which language is used in texts and conversations, often to uncover the relationships between sentences and paragraphs.
• Discourse Analysis: the study of the ways in which language is used in texts and social contexts, often used to understand the relationships between sentences and paragraphs in a piece of text.
• Discourse Analysis: the study of the ways in which language is used in the context of a text or conversation, in order to understand the relationships between different parts of the text or conversation.
• Discourse Analysis: the study of the ways in which language is used to express meaning in longer stretches of text, such as in conversations or written texts.
• Discourse Analysis: the study of the ways in which language is used to organize and connect ideas in text, used to understand the underlying meaning and context of a text.
• Discourse Markers: words or phrases that signal the organization and relationships between clauses, sentences, and discourse segments.
• Discourse: the use of language in a broader conversational or discourse context, focusing on the organization and coherence of text or talk.
• Doc2Vec: a technique for representing documents as vectors, similar to word embeddings, but taking into account the order of the words in the document.
• Doc2Vec: a technique to generate dense vector representation of a document, it is an extension of word2vec technique.
• ELMO: a deep bidirectional language model that uses a combination of character-based and token-based representations to improve the performance of a wide range of natural language processing tasks.
• ELMO: a deep learning model that learns to represent words in a way that is useful for a variety of natural language processing tasks. ELMO stands for “Embeddings from Language Models.”
• ELMO: a pre-trained deep bidirectional language model for natural language processing tasks.
• Emotion Detection: the task of identifying and classifying emotions, such as happiness, sadness, and anger, in a piece of text or speech.
• Event Extraction: the process of identifying and extracting information about events, such as when they occurred, where they occurred and who was involved, from unstructured text.
• Event Extraction: the task of identifying and extracting events and their arguments from a piece of text, such as the who, what, where, when, and why of an event.
• FLOPs: A measure of computational complexity of an NLP model, FLOPS stands for floating point operations per second.
• Frame Semantics: the study of how words and phrases are used in context to convey meaning, often based on a set of predefined frames or scenarios.
• GloVe: a technique to generate dense vector representation of words, it is an extension of word2vec technique.
• GPT: Generative Pre-training Transformer, a large pre-trained transformer-based neural network model for natural language processing tasks such as language translation, text summarization, and text generation.
• GPT-2: a transformer-based language model that can generate human-like text, and can be fine-tuned for a wide range of natural language processing tasks, such as text generation, text completion, and text summarization.
• GPT-2: An upgraded version of GPT with larger model size and better performance on various NLP tasks.
• GPT-3: a transformer-based deep learning model for natural language processing tasks such as text generation, translation, and summarization. GPT stands for “Generative Pre-trained Transformer.”
• GPT-3: An even larger version of GPT-2 with better performance on various NLP tasks, it is considered to be the state of the art model in NLP.
• Grapheme-to-Phoneme Conversion: the process of converting written text into speech, often used to improve the performance of text-to-speech systems.
• Information Extraction: the process of automatically extracting structured information from unstructured or semi-structured text.
• Information Extraction: the task of automatically extracting structured information from unstructured text, such as named entities or facts.
• Information Extraction: the task of automatically extracting structured information from unstructured text.
• Information Retrieval: the process of searching for and retrieving information from a collection of documents or other data sources.
• Knowledge Graph Construction: the task of automatically building a graph-based representation of knowledge from text data.
• Knowledge Graph: a graph-based representation of knowledge, where entities are represented as nodes and relationships between entities are represented as edges.
• Knowledge Graph: a graph-based representation of knowledge, where entities are represented as nodes and relationships between them as edges.
• Knowledge Graph: a representation of real-world entities and their relationships, often used to power search engines and intelligent assistants.
• Language Identification: the process of determining the language of a piece of text.
• Language Identification: the process of identifying the language of a given text.
• Language Identification: the task of determining the language of a given text.
• Language Identification: the task of determining the language of a piece of text.
• Language Identification: the task of identifying the language of a given text.
• Language Model: a model that assigns a probability to a sequence of words, typically used for tasks such as text generation, machine translation, and speech recognition.
• Language Model: a statistical model that is trained to predict the next word in a sentence based on the context of the previous words.
• Language Modeling with RNN: the process of language modeling with the help of Recurrent Neural Network.
• Language Modeling with Transformer: the process of language modeling with the help of Transformer based architecture.
• Language Modeling: the process of predicting the probability distribution of words in a sentence or text given the previous words.
• Language Modeling: the task of learning the probability distribution of words in a text corpus, in order to generate new text that is similar in style and content to the training data.
• Language Modeling: the task of predicting the next word in a sentence based on the previous words.
• Language Modeling: the task of predicting the next word in a sentence or sequence of words, often used to train and evaluate language models.
• Language Modeling: the task of predicting the next word in a sentence or text given a context, often used for text generation, speech recognition, and other NLP tasks.
• Language Modeling: the task of predicting the next word in a sentence, given the previous words.
• Language Modeling: the task of predicting the next word in a sequence of words based on the previous words.
• Language Modeling: the task of predicting the next word in a sequence of words, based on the previous words, by training a model on a large corpus of text.
• Language Translation: the process of converting text from one language to another.
• Latent Dirichlet Allocation (LDA): a technique used to discover the latent topics in a corpus of text, by identifying the probability distribution over words for each topic.
• Latent Semantic Analysis (LSA): a technique used to analyze the relationships between words in a text, based on their co-occurrence patterns and the underlying latent semantic structure of the text.
• Lemmatization: the process of reducing a word to its base form, also called the lemma, which is useful for comparing words in different forms.
• Lemmatization: the process of reducing a word to its base form, based on its context and inflection, often used to improve the performance of text analysis algorithms.
• Lemmatization: the process of reducing a word to its base form, known as a lemma, while taking into account its grammatical context.
• Lemmatization: the process of reducing a word to its base form, often used to improve the accuracy of text analysis and natural language processing tasks.
• Lemmatization: the process of reducing a word to its base or root form, for example “running” to “run” but with the consideration of context in which word is used.
• Lemmatization: the process of reducing a word to its base or root form.
• Lexicon: the set of words and phrases in a language and their meanings.
• Machine Translation: the process of automatically translating text from one language to another using computational methods.
• Machine Translation: the process of automatically translating text from one language to another.
• Machine Translation: the process of using computational methods to translate text from one language to another.
• Machine Translation: the task of automatically translating text from one language to another using machine learning algorithms.
• Machine Translation: the task of automatically translating text from one language to another.
• Machine Translation: the task of translating text from one language to another using a machine.
• Machine Translation: the task of translating text from one language to another using computer algorithms.
• Named Entity Disambiguation (NED): the task of determining the real-world object or concept that a named entity refers to, for example, that “Barack Obama” refers to the 44th President of the United States.
• Named Entity Recognition (NER): the process of identifying and classifying entities such as people, organizations, and locations in a piece of text.
• Named Entity Recognition (NER): the process of identifying and classifying named entities in text, such as people, organizations, and locations.
• Named Entity Recognition (NER): the process of identifying and classifying named entities in text, such as people, organizations, locations, and dates.
• Named Entity Recognition (NER): the process of identifying and classifying named entities, such as people, organizations, and locations, in a piece of text.
• Named Entity Recognition (NER): the task of identifying and classifying named entities in text, such as people, organizations, and locations.
• Named Entity Recognition (NER): the task of identifying and classifying named entities such as person names, organization names, location names, and so on in a piece of text.
• Named Entity Recognition (NER): the task of identifying and classifying named entities such as person names, organizations, locations, etc. in unstructured text.
• Named Entity Recognition (NER): the task of identifying and classifying named entities, such as people, organizations, and locations, in a piece of text.
• Named Entity Recognition (NER): the task of identifying and classifying named entities, such as people, organizations, and locations, in a text.
• Named Entity Recognition (NER): the task of identifying and classifying named entities, such as people, organizations, locations, and dates, in a text.
• Named Entity Recognition (NER): the task of identifying and classifying named entities, such as person names, organizations, and locations, in a piece of text.
• Named Entity Recognition (NER): the task of identifying and classifying named entities, such as persons, organizations, and locations, in a piece of text.
• Named Entity Recognition: the task of identifying and classifying named entities such as people, organizations, and locations in a text.
• Natural Language Generation (NLG): the task of automatically generating natural language text or speech from structured data.
• Natural Language Generation (NLG): the task of automatically generating natural language text, such as in the form of a summary or a response to a question.
• Natural Language Processing (NLP): a subfield of artificial intelligence and computational linguistics that deals with the interaction between computers and human language.
• Natural Language Understanding (NLU): the task of automatically extracting meaning from natural language text or speech.
• Natural Language Understanding (NLU): the task of extracting meaningful information from natural language text or speech.
• n-grams: a contiguous sequence of n items from a given sample of text or speech, where n is the number of items in the sequence.
• NLP pipeline: a sequence of natural language processing tasks that are applied to a piece of text, such as tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis.
• Ontology and Knowledge Representation: the task of representing knowledge in a structured way, often used in natural language question answering systems.
• Ontology: a formal representation of knowledge as a set of concepts within a domain and the relationships between those concepts.
• Opinion Mining: the task of identifying and extracting subjective information from text, such as opinions, evaluations, appraisals, and appraisers.
• Parsing: the process of analyzing a sentence and determining its grammatical structure.
• Parsing: the process of analyzing a sentence, phrase or text and breaking it down into its component parts, such as noun phrases, verb phrases, and clauses.
• Parsing: the process of analyzing and understanding the grammatical structure of a sentence.
• Parsing: the process of analyzing the structure of a sentence or piece of text, often used to understand the grammatical relationships between words and phrases.
• Part-of-Speech (POS) Tagging: the process of labeling words in a sentence with their corresponding parts of speech, such as noun, verb, adjective, etc.
• Part-of-Speech (POS) Tagging: the process of marking up the words in a text with their corresponding POS, such as noun, verb, adjective, etc.
• Part-of-Speech (POS) Tagging: the task of identifying the grammatical function of each word in a sentence, such as noun, verb, adjective, etc.
• Part-of-Speech Tagging (POS Tagging): the process of identifying and labeling the parts of speech of each word in a sentence, such as nouns, verbs, adjectives, and adverbs.
• Part-of-speech Tagging (POS tagging): the process of identifying the grammatical role of each word in a sentence, such as noun, verb, adjective, etc.
• Part-of-Speech Tagging (POS Tagging): the task of assigning a grammatical category, such as noun, verb, adjective, to each word in a text.
• Part-of-Speech Tagging (POS Tagging): the task of identifying and classifying the grammatical category of words in a sentence, such as nouns, verbs, adjectives, and so on.
• Part-of-Speech Tagging (POS Tagging): the task of identifying and labeling the parts of speech, such as nouns, verbs, and adjectives, of the words in a piece of text.
• Part-of-Speech Tagging (POS): the process of identifying and classifying words in a piece of text by their grammatical function, such as noun, verb, adjective, etc.
• Part-of-Speech Tagging (POS): the process of labeling the words in a sentence with their grammatical role, such as noun, verb, adjective, etc.
• Part-of-Speech Tagging: the task of assigning a grammatical category, such as noun, verb, or adjective, to each word in a sentence.
• Pragmatics Analysis: the study of how context influences the meaning of language.
• Pragmatics: the branch of linguistics concerned with the ways in which speakers use language in context, such as how meaning is conveyed through implicature and presupposition.
• Pragmatics: the study of how context influences the meaning of language.
• Pragmatics: the study of how language is used in context, including the social and cultural factors that influence meaning.
• Question Answering (QA): the task of answering questions posed in natural language, often using a combination of language understanding and knowledge retrieval.
• Question Answering (QA): the task of automatically answering questions posed in natural language.
• Question Answering: the task of automatically answering questions in natural language based on a given text or knowledge base.
• Question Answering: the task of providing a specific answer to a question in natural language, often used to build intelligent assistants or chatbots.
• Relationship Extraction: the process of identifying and extracting relationships between entities from unstructured text.
• RoBERTa: A robustly optimized BERT pre-training approach which yields better performance on various NLP tasks.
• RoBERTa: a transformer-based language model that is trained on a much larger dataset than BERT and fine-tuned using a technique called dynamic masking.
• Semantic Analysis: the process of understanding the meaning of words and phrases in a piece of text, and how they relate to each other.
• Semantic Parsing: the process of analyzing the meaning of a sentence, such as identifying the entities and relationships mentioned in the sentence.
• Semantic Role Labeling (SRL): the task of analyzing the semantic roles of words and phrases in a sentence, such as the agent, patient, and instrument of an action.
• Semantic Role Labeling (SRL): the task of identifying the semantic roles of each word in a sentence, such as the agent, patient, or instrument of an action.
• Semantic Role Labeling (SRL): the task of identifying the semantic roles of the different elements in a sentence, such as the subject, object, and predicate.
• Semantic Role Labeling (SRL): the task of identifying the semantic roles played by different words in a sentence, such as the subject, object, and verb.
• Semantic Role Labeling: the process of analyzing the semantic roles of words in a sentence, such as identifying the agent, patient, and theme of a verb.
• Semantic Role Labeling: the process of identifying and labeling the semantic roles of words or phrases in a sentence, such as the subject, object, and agent of a verb.
• Semantic Role Labeling: the process of identifying the semantic roles of words in a sentence, such as the agent, patient, and instrument.
• Semantic Role Labeling: the task of identifying the arguments and their roles in a sentence, such as the subject, object, and verb.
• Semantic Role Labeling: the task of identifying the arguments of a predicate and their semantic roles in a sentence.
• Semantic Role Labeling: the task of identifying the roles or arguments played by different words in a sentence, such as the subject, object, and predicate.
• Semantics: the branch of linguistics concerned with the meaning of words, phrases, sentences, and text.
• Semantics: the study of the meaning of words, phrases, and sentences in a language.
• Sentence Boundary Detection: the process of identifying the boundaries between sentences in a text.
• Sentence Boundary Detection: the task of identifying the boundaries between sentences in a text.
• Sentence Boundary Detection: the task of identifying the boundaries of sentences in a piece of text.
• Sentence Compression: the process of creating a shorter version of a sentence that preserves its main meaning.
• Sentence Embedding: a technique for representing sentences or short text segments as dense numerical vectors in a high-dimensional space, such that similar sentences are close together in the space.
• Sentence Embeddings: a technique for representing a sentence as a single vector, typically obtained by averaging the word embeddings of its words.
• Sentence Similarity: the process of determining the similarity between two sentences or phrases
• Sentence Simplification: the process of rewriting a sentence in a simpler form while retaining the core meaning.
• Sentiment Analysis: the process of determining the emotional tone or attitude of a piece of text, such as whether it is positive, negative, or neutral.
• Sentiment Analysis: the process of determining the sentiment or emotional tone of a piece of text, usually categorized as positive, negative, or neutral.
• Sentiment Analysis: the task of determining the sentiment or emotion conveyed in a piece of text, such as positive, negative, or neutral.
• Sentiment Analysis: the task of determining the sentiment or emotion conveyed in a text, such as positive, neutral or negative.
• Sentiment Analysis: the task of determining the sentiment or emotion expressed in a piece of text, often used to gauge public opinion on a topic.
• Sentiment Analysis: the task of determining the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral.
• Sentiment Analysis: the task of determining the sentiment or emotion expressed in a piece of text, such as whether it is positive, negative, or neutral.
• Sentiment Analysis: the task of determining the sentiment or emotion expressed in text, such as positive, negative, or neutral.
• Sentiment Analysis: the task of determining the sentiment or emotional tone of a piece of text, often used to classify text as positive, negative, or neutral.
• Sentiment Analysis: the task of determining the sentiment or emotional tone of a piece of text, such as positive, negative, or neutral.
• Sentiment Analysis: the task of determining the sentiment or emotional tone of a piece of text.
• Sentiment Analysis: the task of determining the sentiment or emotional tone of a text, such as positive, negative, or neutral.
• Sentiment Analysis: the task of determining the sentiment or opinion expressed in a text, often as positive, negative, or neutral.
• Shallow Parsing: the process of analyzing a sentence to identify the syntactic structure at a shallow level, such as part-of-speech tags, chunks, or named entities.
• Speech Recognition: the process of converting spoken language to text.
• Speech recognition: the process of converting spoken words into text.
• Speech Recognition: the process of converting spoken words into written text.
• Speech Recognition: the task of converting spoken audio into written text.
• Speech Recognition: the task of converting spoken language into text.
• Speech Recognition: the task of converting spoken speech into written text.
• Speech Synthesis: the process of generating speech from text or other symbolic representation.
• Speech Synthesis: the process of generating spoken language from text or other symbolic representation.
• Speech Synthesis: the task of converting text into spoken language.
• Speech Synthesis: the task of converting written text into spoken audio.
• Speech Synthesis: the task of generating spoken speech from written text.
• Speech-to-text (STT): the process of converting spoken words into text
• Speech-to-Text: the process of converting spoken language to text.
• Speech-to-Text: the task of converting spoken words into written text.
• Stemming and Lemmatization: the tasks of reducing words to their base or dictionary forms in order to normalize them for text processing tasks such as text classification or information retrieval.
• Stemming: the process of reducing a word to its base form, often used to improve the performance of text analysis algorithms.
• Stemming: the process of reducing a word to its base or root form, for example “running” to “run”
• Stemming: the process of reducing a word to its base or root form, such as reducing “running” to “run.”
• Stemming: the process of reducing a word to its root form, often used to improve the accuracy of text analysis and natural language processing tasks.
• Stemming: the process of reducing a word to its stem, which is the part of the word that is common to all its inflected forms.
• Stop Words: a list of common words that are typically filtered out before or after processing text, such as “a,” “an,” “the,” “and,” and so on.
• Summarization: the task of condensing a piece of text into a shorter version while still retaining its important information.
• Syntactic Parsing: the process of analyzing the grammatical structure of a sentence, such as identifying the subject, predicate, and objects.
• Syntactic Parsing: the process of analyzing the syntactic structure of a sentence, often used to understand the grammatical relationships between words and phrases.
• Syntactic Parsing: the process of analyzing the syntactic structure of a sentence, such as identifying noun phrases, verb phrases, and other constituents.
• Syntactic Parsing: the task of analyzing the grammatical structure of a sentence, often represented as a tree-like structure.
• Syntax Parsing: the process of analyzing the grammatical structure of a sentence, such as identifying its constituents and dependencies.
• Syntax Parsing: the task of analyzing the grammatical structure of a sentence, often represented as a tree-like structure called a parse tree.
• Syntax: the branch of linguistics concerned with the rules for constructing grammatically correct sentences in a language.
• Syntax: the study of the rules governing the structure of sentences in a language.
• T5: a transformer-based language model that is trained using a technique called denoising, which improves the performance on a wide range of natural language processing tasks, such as text-to-text generation, text classification, and question answering.
• T5: Pre-trained model for a wide range of natural language understanding and generation tasks, it is considered to be the state of the art model in NLP.
• Temporal Expressions Recognition and Normalization (TERN): the task of identifying and normalizing temporal expressions, such as dates and times, in a piece of text.
• Text Augmentation: the process of creating new variations of text data to increase the size of a dataset for training machine learning models.
• Text Augmentation: The process of generating new text by making slight modifications to existing text, such as changing words or phrases, to improve the performance of an NLP model.
• Text Augmentation: the process of generating new text by modifying existing text, often used in data augmentation for machine learning tasks.
• Text Augmentation: the task of generating new training data by applying various operations on the original data, such as replacing words with synonyms or adding noise to the text.
• Text Classification: the process of assigning predefined categories or labels to a piece of text, such as spam or not spam, positive or negative sentiment, etc.
• Text Classification: the process of assigning predefined categories or labels to text based on its content.
• Text Classification: the process of automatically assigning a label or category to a piece of text based on its content.
• Text Classification: the task of assigning a predefined category or label to a piece of text, such as classifying an email as spam or not.
• Text Classification: the task of assigning a predefined category or label to a piece of text.
• Text Classification: the task of assigning a predefined set of categories or labels to a piece of text.
• Text classification: the task of assigning one or more predefined categories or labels to a piece of text, such as spam/not spam, positive/negative sentiment, or topic classification.
• Text Classification: the task of assigning predefined categories or labels to a given text.
• Text Classification: the task of assigning predefined categories or labels to a piece of text, such as spam or not spam, or positive or negative sentiment.
• Text Classification: the task of assigning predefined categories or labels to a text, based on its content.
• Text Classification: the task of assigning predefined categories or labels to a text, such as spam or not spam, or positive or negative sentiment.
• Text Classification: the task of assigning predefined categories or labels to text, such as spam detection, topic classification, and sentiment analysis.
• Text Classification: the task of categorizing a text into predefined categories or labels, such as spam or not spam, or positive or negative sentiment.
• Text Classification: the task of categorizing text into predefined categories or classes.
• Text Clustering: the process of grouping similar documents or pieces of text together based on their content.
• Text Clustering: the process of grouping similar pieces of text together.
• Text Clustering: the task of grouping similar pieces of text together into clusters.
• Text Clustering: grouping similar texts together based on their content or features.
• Text Clustering: the task of grouping similar texts together based on their content or semantic similarity.
• Text Extraction: the process of extracting specific information from a piece of text, such as dates, phone numbers, or addresses.
• Text generation : the task of automatically generating new text that is similar to a given input text.
• Text generation based on genre: the task of generating text in a specific genre, such as poetry, news articles, or fiction.
• Text generation based on image captioning: the task of generating a caption for a given image.
• Text generation based on structure: the task of generating text that follows a specific structure, such as a recipe, a script, or a technical report.
• Text generation based on style: the task of generating text in a specific style or tone, such as formal or informal, serious or humorous.
• Text generation based on video captioning: the task of generating a caption for a given video.
• Text Generation with Encoder-Decoder: the task of generating new text based on a given input text by training a neural network model with an encoder and a decoder.
• Text Generation with GAN: The process of creating new text with the help of a Generative Adversarial Network.
• Text Generation with GPT: The process of creating new text with the help of a pre-trained language model such as GPT-3 by OpenAI.
• Text Generation with GPT-3: the task of generating new text based on a given input text by training a neural network model with GPT-3 architecture.
• Text Generation with Transformer: the task of generating new text based on a given input text by training a neural network model with a transformer architecture.
• Text Generation: the process of automatically generating new text based on a given input or model.
• Text Generation: the process of creating new text that is similar in style or content to a given input text, often used to generate summaries, captions, or other types of text.
• Text Generation: the process of generating new text based on a given input, such as a summary or a prompt.
• Text Generation: the task of automatically creating new text based on a given input or set of inputs.
• Text Generation: automatically generating coherent and natural text, based on a given input or model.
• Text Generation: the task of automatically generating new text based on a given input or model.
• Text Generation: the task of automatically generating new text that is similar to a given input text.
• Text Generation: the task of creating new text that is like existing text, often used in language modeling and creative writing applications.
• Text Generation: the task of generating natural language text based on a given prompt or set of constraints.
• Text Generation: the task of generating new text based on a given input or model, such as generating a summary of a news article or generating a response to a question.
• Text Generation: the task of generating new text based on a given input, such as a prompt or a model trained on a dataset.
• Text generation: the task of generating new text that is coherent and appropriate for a given context, such as text completion, text summarization, and text-to-text generation.
• Text normalization: the process of converting text into a standard or normalized form to facilitate its processing and analysis.
• Text Normalization: the process of converting text into a standardized format, such as lowercasing all words or replacing slang and informal language with more formal equivalents.
• Text Normalization: the process of transforming a piece of text into a standard form, such as lowercasing all the words or stemming the words.
• Text Normalization: the task of converting text to a standard form, such as lowercasing all words or removing punctuation.
• Text Segmentation: the process of dividing a text into smaller chunks, such as sentences or paragraphs.
• Text Similarity: the process of determining the degree of similarity between two pieces of text, often used in information retrieval and text mining.
• Text Similarity: the process of measuring the similarity between two pieces of text, such as cosine similarity, Jaccard similarity, etc.
• Text Similarity: the task of determining the similarity between two or more texts, often used for plagiarism detection and information retrieval.
• Text Similarity: the task of determining the similarity or relatedness between two pieces of text.
• Text Similarity: the task of measuring the similarity between two pieces of text, typically done by comparing their sentence or document embeddings.
• Text Similarity: the task of measuring the similarity between two pieces of text.
• Text Simplification: the process of rewriting text to make it easier to understand while retaining its core meaning.
• Text Simplification: the process of simplifying a piece of text by reducing its complexity and making it easier to understand.
• Text Simplification: the process of simplifying text to make it easier for a specific audience to understand, such as reducing the complexity of language for non-native speakers or children.
• Text Simplification: the task of converting complex text into simpler text that is easier to understand.
• Text simplification: the task of making text easier to understand for a specific audience, such as non-native speakers, children, or people with reading difficulties.
• Text Simplification: the task of modifying text to make it easier to understand for a specific audience, such as non-native speakers or individuals with reading difficulties.
• Text Simplification: the task of rewriting text to make it easier to understand for a particular audience or level of education.
• Text Style Transfer: The process of changing the style of text, such as changing the tone, formality, or sentiment of a piece of text.
• Text Summarization with Abstractive : the task of generating new text that summarizes the main idea of a given input text.
• Text Summarization with Extractive: the task of selecting the most informative text segments and concatenating them to form a summary.
• Text Summarization: the process of creating a condensed version of a piece of text that captures the main points or ideas.
• Text Summarization: the process of creating a summary of a piece of text, such as extracting key points or identifying the main idea.
• Text Summarization: the task of automatically creating a shorter version of a text that still conveys its main ideas and information.
• Text Summarization: the task of automatically generating a shorter version of a piece of text that retains the most important information.
• Text Summarization: the task of condensing a text to its most important information, often done by extracting key sentences or phrases.
• Text summarization: the task of creating a shorter version of a text that conveys its most important information.
• Text Summarization: the task of creating a shorter version of a text that preserves the most important information.
• Text Summarization: the task of creating a shorter version of a text that retains its most important information.
• Text Summarization: the task of generating a concise and coherent summary of a text, that captures its main ideas and important information.
• Text Summarization: the task of generating a short summary of a longer text.
• Text Summarization: the task of generating a shorter version of a text that conveys its main ideas or information.
• Text Summarization: the task of generating a shorter version of a text while retaining its main ideas and key information.
• Text Tagging: the process of adding additional information to text, such as part-of-speech tags or named entity labels.
• Text-to-3D model synthesis : the task of generating a 3D model based on a text input.
• Text-to-Action: the process of converting natural language text into an actionable command or instruction, such as a query to a database or a command to a device.
• Text-to-ASCII : the task of converting written text into ASCII characters.
• Text-to-Braille : the task of converting written text into braille script.
• Text-to-code : the task of generating code based on a text input.
• Text-to-Code: the process of converting natural language text into code or programming languages.
• Text-to-Code: the task of generating code from natural language descriptions.
• Text-to-Emoji : the task of converting written text into emojis.
• Text-to-Form: the process of converting natural language text into a form filled with data.
• Text-to-Gif : the task of converting written text into gif.
• Text-to-Handwriting: the task of converting written text into handwriting characters.
• Text-to-Image : the task of converting written text into image.
• Text-to-image synthesis : the task of generating an image based on a text input.
• Text-to-LaTeX : the task of generating LaTeX code based on a text input.
• Text-to-LaTeX: the task of generating LaTeX code from natural language descriptions.
• Text-to-Markdown : the task of generating Markdown code based on a text input.
• Text-to-Markdown: the task of generating Markdown code from natural language descriptions.
• Text-to-Morse : the task of converting written text into morse code.
• Text-to-Scene: the process of converting natural language text into a scene or visual representation.
• Text-to-Sign Language : the task of converting written text into sign language.
• Text-to-Speech (TTS) : the process of converting written text into spoken words.
• Text-to-Speech (TTS) : the task of converting written text into spoken speech.
• Text-to-Speech (TTS) and Speech-to-Text (STT): the task of converting text to speech and speech to text, respectively.
• Text-to-Speech (TTS) and Speech-to-Text (STT): the tasks of converting text to speech and speech to text respectively, used in applications such as voice assistants and speech recognition systems.
• Text-to-Speech (TTS) and Speech-to-Text (STT): TTS is the process of converting written text into speech, while STT is the process of converting spoken speech into written text.
• Text-to-speech (TTS): the process of converting written text into spoken words.
• Text-to-speech synthesis : the task of generating a speech based on a text input.
• Text-to-Speech synthesis with emotional control : the task of converting written text into spoken speech with control over emotional features such as excitement, happiness, and sadness.
• Text-to-Speech synthesis with multilingual support : the task of converting written text into spoken speech in multiple languages.
• Text-to-Speech synthesis with prosody control : the task of converting written text into spoken speech with control over prosodic features such as pitch, stress, and intonation.
• Text-to-Speech synthesis with voice conversion: the task of converting written text into spoken speech with different voice characteristics.
• Text-to-Speech synthesis: the process of generating speech from text or other symbolic representation.
• Text-to-Speech: the process of generating speech from text, used in applications such as navigation systems, voice assistants, and accessibility technology.
• Text-to-Speech: the task of converting written text into spoken speech.
• Text-to-Speech: the task of converting written text into spoken words.
• Text-to-SQL : The process of converting natural language text into a SQL query.
• Text-to-SQL : the task of generating a SQL query based on a text input.
• Text-to-SQL: The process of converting natural language text into a SQL query.
• Text-to-SQL: the task of converting natural language text into a structured query language (SQL) that can be used to query a database.
• Text-to-SQL: the task of generating SQL queries from natural language questions.
• Text-to-Unicode : the task of converting written text into Unicode characters.
• Text-to-Video : the task of converting written text into video.
• Text-to-video synthesis : the task of generating a video based on a text input.
• Text-to-XML : the task of generating XML code based on a text input.
• Textual Entailment (TE): the task of determining whether a piece of text (the premise) semantically implies another piece of text (the hypothesis).
• Textual Entailment: the process of determining whether one piece of text (a premise) semantically entails another piece of text (a hypothesis), often used in natural language inference and question answering tasks.
• Textual Entailment: the task of determining whether a text implies another text or statement.
• Textual Entailment: the task of determining whether the meaning of one piece of text, called the premise, logically entails the meaning of another piece of text, called the hypothesis.
• Textual Similarity: the task of determining the semantic similarity between two pieces of text.
• Tokenization: the process of breaking a piece of text into individual words, phrases, or other elements.
• Tokenization: the process of breaking a piece of text into its individual words or tokens.
• Tokenization: the process of breaking a text into smaller units called tokens, such as words or sentences.
• Tokenization: the process of breaking down a sentence or a piece of text into individual words or smaller units of meaning, such as phrases or clauses.
• Transformer: a neural network architecture designed to process sequential data such as text. The transformer architecture allows for the parallel processing of the input, which leads to faster training and inference.
• ULMFiT: A pre-training method for any NLP task using transfer learning on a language model.
• ULMFiT: a transfer learning method for natural language processing tasks, which fine-tunes a pre-trained language model on a specific task using a technique called gradual unfreezing.
• Word Embedding: a mathematical representation of a word in a high-dimensional space, where words that have similar meanings are located close to each other.
• Word Embedding: a technique for representing words as dense numerical vectors in a high-dimensional space, such that words that have similar meanings are close together in the space.
• Word Embedding: a technique for representing words in a high-dimensional vector space, where semantically similar words are close to each other in the space.
• Word Embedding: a technique used to represent words as numerical vectors in a high-dimensional space, based on their distributional properties and relationships with other words in a corpus.
• Word Embedding: a way to represent words in a high-dimensional space such that semantically similar words are close to each other.
• Word Embeddings: a representation of words as vectors in a high-dimensional space, where semantically similar words are close to each other in the vector space. Word2Vec and GloVe are examples of algorithms for learning word embeddings.
• Word Embeddings: a representation of words as vectors in a high-dimensional space, where the vectors are learned such that semantically similar words are close to each other in the space.
• Word Embeddings: a technique for representing words in a continuous vector space, where semantically similar words are close to each other in the vector space.
• Word Embeddings: a technique for representing words in a high-dimensional vector space, such that semantically similar words are mapped to similar vectors.
• Word Embeddings: a technique to represent words as dense vectors in a high-dimensional space, often used to improve the performance of natural language processing tasks.
• Word Sense Disambiguation (WSD): the task of determining the correct sense of a word in a given context, as words often have multiple meanings.
• Word Sense Disambiguation (WSD): the task of determining the correct sense or meaning of a word in context.
• Word Sense Disambiguation: the process of determining the correct sense of a word in a given context.
• Word Sense Disambiguation: the process of determining the correct sense or meaning of a word in context, disambiguating it from other possible meanings of the same word.
• Word Sense Disambiguation: the task of determining the intended meaning of a word based on its context.
• Word sense disambiguation: the task of determining the meaning of a word in context, by identifying the sense of the word that is most appropriate in the given context.
• Word Sense Disambiguation: the task of identifying the correct sense of a word in context, when a word has multiple meanings.