Natural Language Processing Terms Glossary: Natural Language Processing Terms in 2024
A
Active Learning
Active Learning is a machine learning approach where a model actively selects and queries informative samples from a large unlabeled dataset to improve its performance.
Anaphora Resolution
Anaphora Resolution is the process of identifying and linking an anaphoric expression to its antecedent.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis is the task of analyzing sentiment towards specific aspects or entities within a piece of text. It involves identifying opinion targets, their attributes, and the sentiment expressed towards them.
Attention Is All You Need
Attention Is All You Need is a transformer-based neural network architecture proposed for various NLP tasks, achieving state-of-the-art results.
Attention Mechanism
Attention Mechanism is a technique used in neural networks to focus on specific parts of the input sequence when generating the output.
Automated Speech Recognition (Asr)
Automated Speech Recognition (ASR) is the technology that converts spoken language into written text. ASR systems are used in applications like transcription services, voice assistants, and voice command interfaces.
B
Bag-Of-Words
Bag-of-Words (BoW) is a simple and commonly used representation of text that only considers the frequency of words in the text, ignoring their order.
Bert
BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based language model pre-trained on large-scale text data, commonly used for various NLP tasks.
Bias In Nlp
Bias in NLP refers to the presence of unfair or discriminatory behavior in NLP models or datasets. It can be due to biased training data or biased algorithmic decisions, leading to biased outcomes or predictions.
Bilstm
Bidirectional Long Short-Term Memory (BiLSTM) is a type of RNN that processes input sequence in both forward and backward directions.
C
Chatbot
A Chatbot is a computer program designed to simulate conversation with human users.
Chunking
Chunking is the process of grouping words together into meaningful chunks, such as noun phrases or verb phrases. It helps in extracting structured information from unstructured text.
Coherence
Coherence is the measure of how well the individual parts of a text fit together and form a unified whole, often assessed in terms of logical connections and flow.
Cohesion
Cohesion refers to the semantic similarity or relatedness between words or phrases in a text, often measured by co-occurrence or distributional patterns.
Concept Extraction
Concept Extraction, also known as Terminology Extraction, is the task of identifying and extracting domain-specific concepts or terms from text.
Contextual Word Embedding
Contextual Word Embedding is a word embedding model that captures the meaning of words based on their context in a sentence or document.
Coreference Resolution
Coreference Resolution is the task of determining when two or more expressions in a text refer to the same entity or concept. It is important for understanding the context and maintaining coherence in natural language understanding.
Corpus
In NLP, a Corpus is a collection of written or spoken text used for training, evaluating, or testing language processing models.
Cross-Lingual Nlp
Cross-lingual NLP is the field that focuses on developing NLP techniques and models that can work across multiple languages. It involves tasks like machine translation, cross-lingual information retrieval, and multilingual text classification.
Cross-Lingual Word Embedding
Cross-lingual Word Embedding is a technique that learns word embeddings that can capture semantic similarities across different languages. It helps in tasks like cross-lingual information retrieval and machine translation.
D
Data Augmentation
Data Augmentation is a technique used to artificially increase the size of a training dataset by creating new samples with slight modifications. In NLP, it can involve methods like back-translation, word replacement, or text paraphrasing.
Deepavlov
DeepPavlov is an open-source NLP library and platform that provides a set of pre-trained models and tools for building natural language processing applications.
Dependency Parsing
Dependency Parsing is the process of analyzing the grammatical structure of a sentence, determining the relationships between words.
Dependency Relationship
A Dependency Relationship is a grammatical relationship between two words in a sentence, where one word depends on another.
Dialogue System
A Dialogue System, also known as a Conversational Agent or Chatbot, is a computer system designed to engage in conversation with humans, providing information or assistance.
Dialogue Systems
Dialogue Systems, also known as conversational agents or chatbots, are systems that can engage in natural language conversations with users. They are used in various applications, such as customer service and virtual assistants.
Document Classification
Document Classification is the task of categorizing documents into predefined labels or classes based on their content.
Document Similarity
Document Similarity is the task of quantifying the similarity or relatedness between two documents. It can be used in applications like plagiarism detection, document clustering, and information retrieval.
Domain Adaptation
Domain Adaptation is the process of transferring knowledge or models from one domain to another, where the target domain may have different characteristics or data distributions.
E
Encoder-Decoder
Encoder-Decoder is a architecture commonly used in sequence-to-sequence models, where an encoder processes the input sequence and a decoder generates the output sequence.
Entity Linking
Entity Linking is the process of linking named entities in text to their corresponding entities in a knowledge graph or database.
Entity Recognition And Linking (Erl)
Entity Recognition and Linking (ERL) is a combined task of Named Entity Recognition (NER) and Named Entity Linking (NEL). It involves identifying named entities in text and linking them to unique identifiers in a knowledge base.
Ethics In Nlp
Ethics in NLP refers to the evaluation and consideration of the ethical implications and consequences of NLP models and applications. It involves addressing issues like bias, fairness, privacy, and transparency in NLP systems.
Evaluation Metrics
Evaluation Metrics are used to assess and measure the performance of NLP models and algorithms. Common evaluation metrics in NLP include accuracy, precision, recall, F1 score, and perplexity.
Event Extraction
Event Extraction is the task of identifying and classifying structured information about events or happenings mentioned in text. It involves detecting event triggers, their arguments, and the relationships between them.
Explainable Ai
Explainable AI (XAI) is the field that focuses on developing AI models and algorithms that can provide interpretable explanations for their decisions and predictions. It is important for building trust and transparency in AI systems.
G
Glove
GloVe (Global Vectors for Word Representation) is a word embedding model that represents words as vectors based on their co-occurrence statistics.
I
Inference
In NLP, Inference refers to the process of deriving logical conclusions or predictions from text or language input.
Information Extraction
Information Extraction is the task of automatically extracting structured information from unstructured text.
K
Knowledge Graph
A Knowledge Graph is a structured representation of knowledge that captures relationships between entities, concepts, and attributes in a domain.
L
Language Generation
Language Generation is the task of generating text that is coherent, fluent, and contextually appropriate, often used in chatbots or dialogue systems.
Language Identification
Language Identification is the task of determining the language or languages used in a given piece of text or multilingual data.
Language Modeling
Language Modeling is the process of predicting the next word in a sequence of words.
Lemmatization
Lemmatization is the process of reducing words to their base or dictionary form, considering their morphological and grammatical characteristics.
Long Short-Term Memory
Long Short-Term Memory (LSTM) is a type of RNN that can effectively capture long-term dependencies in sequential data.
Long Short-Term Memory (Lstm)
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture that addresses the vanishing gradient problem. LSTMs are commonly used in NLP tasks requiring the modeling of long-range dependencies.
Low-Resource Language
A Low-Resource Language is a language with limited available data or resources for building NLP models, often resulting in greater challenges and lower performance.
M
Machine Translation
Machine Translation is the task of automatically translating text from one language to another.
Morphology
Morphology is the study of the internal structure and forms of words in a language.
Multimodal Learning
Multimodal Learning is the task of processing and integrating information from multiple modalities, such as text, images, and audio, to improve performance in various NLP tasks.
Multimodal Nlp
Multimodal NLP is the field that combines natural language processing with other modalities, such as images, videos, or audio. It involves tasks like image captioning, speech recognition, and video summarization.
N
N-Grams
N-grams are contiguous sequences of n items (words or characters) from a given sample of text. They are widely used in language modeling, text generation, and information retrieval.
Named Entity
A Named Entity is a real-world object such as a person, organization, location, product, or date that can be recognized and classified.
Named Entity Classification
Named Entity Classification is the task of assigning predefined categories or labels to named entities in a text, such as person, location, organization.
Named Entity Disambiguation
Named Entity Disambiguation is the process of resolving the ambiguity of named entities, determining the correct entity referred to in a given context.
Named Entity Linking
Named Entity Linking (NEL) is the process of linking named entities in text to a knowledge base, resolving references and disambiguating entities.
Named Entity Linking (Nel)
Named Entity Linking (NEL) is the task of linking named entities in text to unique identifiers in a knowledge base. It connects entities mentioned in unstructured text to structured knowledge sources.
Named Entity Normalization
Named Entity Normalization is the process of converting named entities to a standard or canonical form, for improved consistency and accuracy.
Named Entity Recognition
Named Entity Recognition (NER) is the task of identifying and classifying named entities in text, such as names of people, organizations, and locations.
Named Entity Recognition (Ner)
Named Entity Recognition (NER) is the task of identifying and classifying named entities (such as person names, locations, organizations) in text. NER is useful in various NLP applications, like information extraction and question answering systems.
Natural Language Generation
Natural Language Generation (NLG) is the task of generating human-like text or speech from structured data or other inputs.
Natural Language Processing (Nlp)
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language. NLP enables computers to understand, interpret, and generate human language in a meaningful way.
Natural Language Understanding
Natural Language Understanding (NLU) is a subfield of NLP that focuses on enabling computer systems to understand and interpret human language.
Neural Machine Translation
Neural Machine Translation (NMT) is an approach to machine translation that uses neural networks to model the mapping between source and target languages.
Neural Machine Translation (Nmt)
Neural Machine Translation (NMT) is an approach to machine translation that uses neural network models to generate translations. It has achieved state-of-the-art performance on various language pairs.
Nlp
Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and human language.
Nlp Pipeline
NLP Pipeline refers to the sequence of processing steps or modules applied to a piece of text to perform various NLP tasks. It typically involves steps like tokenization, POS tagging, parsing, and named entity recognition.
O
Ontology
An Ontology is a formal representation of a set of concepts, relationships, and properties within a domain.
Ontology Population
Ontology Population is the task of automatically augmenting or extending an existing ontology with new instances or entities extracted from text or other sources.
P
Parallel Corpora
Parallel Corpora are collections of texts in multiple languages that are pairs or translations of each other. They are used for various multilingual NLP tasks, such as machine translation, cross-lingual retrieval, and cross-lingual word sense disambiguation.
Part-Of-Speech (Pos) Tagging
Part-of-speech (POS) tagging is the process of assigning grammatical categories (such as noun, verb, adjective) to words in a sentence. POS tagging helps in understanding the syntactic structure of a sentence.
Phonetics
Phonetics is the branch of linguistics that deals with the sounds of human speech and their physical properties.
Pos Tagging
Part-of-Speech (POS) Tagging is the process of assigning grammatical tags to words in a sentence. Examples of POS tags include noun, verb, adjective.
Preprocessing
Preprocessing refers to the steps taken to clean, normalize, and transform raw text data before feeding it into a language processing model.
Privacy In Nlp
Privacy in NLP refers to the protection and confidentiality of personal or sensitive information contained in textual data. It involves techniques like anonymization, data encryption, and secure data storage and transmission.
Q
Question Answering
Question Answering is the task of automatically providing accurate answers to questions posed in natural language. It involves understanding the question, searching for relevant information, and generating a suitable response.
R
Recurrent Neural Network
A Recurrent Neural Network (RNN) is a type of artificial neural network that can process sequential data, capturing dependencies over time.
Recurrent Neural Networks (Rnn)
Recurrent Neural Networks (RNN) are a type of neural network commonly used in NLP tasks. RNNs have memory cells that can process sequential data, making them suitable for tasks involving sequence generation or prediction.
Recursive Neural Network
A Recursive Neural Network (RecNN) is a type of artificial neural network that can process structured data with recursive structures, such as parse trees.
Reinforcement Learning
Reinforcement Learning is a type of machine learning that involves an agent interacting with an environment and learning optimal actions through trial and error.
Reproducibility
Reproducibility refers to the ability to recreate and validate research results or experimental findings. In NLP, it is important to provide detailed descriptions, code, and data to enable other researchers to reproduce and build upon existing work.
S
Semantic Role Labeling
Semantic Role Labeling (SRL) is the process of assigning semantic roles to words or phrases in a sentence, indicating their relationship with the main verb.
Semantic Role Labeling (Srl)
Semantic Role Labeling (SRL) is the task of assigning semantic roles to words or phrases in a sentence. It helps in understanding the roles and relationships of entities in a sentence.
Sentiment Analysis
Sentiment Analysis, also known as Opinion Mining, is the process of determining the sentiment or emotion expressed in a piece of text.
Sequence-To-Sequence (Seq2Seq)
Sequence-to-Sequence (Seq2Seq) models are a type of neural network architecture used for tasks involving sequence input and sequence output, such as machine translation and chatbot systems.
Srl Role Classification
Semantic Role Labeling (SRL) Role Classification is the task of assigning semantic roles to words or phrases in a sentence, indicating their specific role or function in relation to the main verb.
Stemming
Stemming is the process of reducing words to their base or root form to normalize variations of words.
Stop Words
Stop Words are commonly used words, such as 'and', 'the', 'is', that are often removed during text preprocessing as they do not carry significant meaning.
Supervised Learning
Supervised Learning is a type of machine learning where a model learns from a labeled dataset to make predictions or classify new instances based on the learned patterns or relationships.
Syntactic Parsing
Syntactic Parsing, also known as Syntax Parsing or Parsing, is the process of analyzing the syntactic structure of a sentence.
Syntax
Syntax refers to the rules that govern the structure of sentences in a language.
T
Term Frequency-Inverse Document Frequency (Tf-Idf)
Term Frequency-Inverse Document Frequency (TF-IDF) is a numerical statistic that reflects the importance of a word in a document corpus. It considers both the frequency of the word in a document and its rarity across the corpus.
Text Classification
Text Classification, also known as Text Categorization, is the process of assigning predefined categories or labels to text documents based on their content.
Text Mining
Text Mining, also known as Text Data Mining, is the process of extracting meaningful patterns and insights from large amounts of text data.
Text Summarization
Text Summarization is the process of generating a concise and coherent summary of a longer piece of text.
Text-To-Speech (Tts)
Text-to-Speech (TTS) is the technology that converts written text into spoken words. TTS systems are used in applications like voice assistants, audiobooks, and accessibility tools for visually impaired individuals.
Tf-Idf
Term Frequency-Inverse Document Frequency (TF-IDF) is a numerical statistic that reflects the importance of a word to a document within a corpus.
Tokenization
Tokenization is the process of breaking text into smaller units called tokens.
Topic Classification
Topic Classification is the task of assigning predefined topics or categories to text documents. It helps in organizing and categorizing large collections of documents.
Topic Modeling
Topic Modeling is a technique used to uncover the main themes or topics in a collection of texts, typically using unsupervised machine learning algorithms.
Transfer Learning
Transfer Learning is a technique where a pre-trained model is used as a starting point for a new task or domain. In NLP, transfer learning has been successfully used for various tasks like sentiment analysis, text classification, and question answering.
Transformer
Transformer is a type of deep learning model used for sequence-to-sequence tasks, such as machine translation and text generation. It utilizes self-attention mechanism to capture global dependencies.
U
Unsupervised Learning
Unsupervised Learning is a type of machine learning where an algorithm learns patterns or structures in data without explicit supervision or predefined labels.
W
Word Alignment
Word Alignment is the process of aligning words or phrases in parallel texts, often used in machine translation and bilingual text analysis.
Word Embedding
Word Embedding is a technique used to represent words as dense vectors, capturing semantic relationships between words.
Word Sense Alignment
Word Sense Alignment is the process of aligning word senses or meanings in different languages or across different resources, for better interoperability and understanding.
Word Sense Disambiguation
Word Sense Disambiguation (WSD) is the process of determining the correct meaning of a word in a given context.
Word Sense Disambiguation (Wsd)
Word Sense Disambiguation (WSD) is the task of determining the correct meaning of a word in a given context. It is important in tasks that require accurate understanding of word semantics, such as machine translation and information retrieval.
Word Sense Induction
Word Sense Induction is the process of automatically identifying and clustering words that have similar meanings or senses in a given context.
Word Sense Induction (Wsi)
Word Sense Induction (WSI) is the task of automatically clustering words with similar meanings or senses together. It is used to discover word senses or meanings without relying on predefined senses or dictionaries.
Word2Vec
Word2Vec is a word embedding model that represents words as vectors based on their distributional properties.
Z
Zero-Shot Learning
Zero-shot Learning is an approach in machine learning where a model is trained to recognize and classify new classes that were not seen during training.