Natural Language Processing

How machines read, understand, and generate human language.

14 articles · 45,208 words

Articles

• Natural language processing

  Natural language processing (NLP) is the processing of natural language information by a computer.

• Language model

  A language model is a computational model that predicts sequences in natural language.

• Word embedding

  In natural language processing, a word embedding is a representation of a word.

• Word2vec

  Word2vec is a technique in natural language processing for obtaining vector representations of words.

• Machine translation

  Machine translation is the use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic, and pragmatic nuances of both languages.

• Speech recognition

  Speech recognition (automatic speech recognition (ASR), computer speech recognition, or speech-to-text (STT)) is a sub-field of computational linguistics concerned with methods and technologies that translate spoken language into text or other interpretable forms.

• Question answering

  Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language.

• Chatbot

  A chatbot (originally chatterbot) is a software application or web interface designed to converse through text or speech, functioning as a conversation partner.

• Text summarization

  Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content.

• Sentiment analysis

  Sentiment analysis (also known as opinion mining) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

• Named-entity recognition

  Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names (PER), organizations (ORG), locations (LOC), geopolitical entities (GPE), vehicles (VEH), medical codes, time expressions, quantities, monetary values, percentages, etc.

• Part-of-speech tagging

  In corpus linguistics, part-of-speech tagging (POS tagging, PoS tagging, or POST), also called grammatical tagging, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.

• Information retrieval

  Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need.

• Topic model

  In natural language processing, a topic model is a type of probabilistic, neural, or algebraic model for discovering the abstract topics that occur in a collection of documents.