ā” Welcome to the SolveForce Store! ā”
Browse freely, explore our products, and see how SolveForce powers the future of connectivity, cloud, and commerce. Dismiss
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.
