Computational linguistics in the context of "Parse tree"

Natural language processing (NLP) is the processing of natural language information by a computer. The study of NLP, a subfield of computer science, is generally associated with artificial intelligence. NLP is related to information retrieval, knowledge representation, computational linguistics, and more broadly with linguistics.

Major processing tasks in an NLP system include: speech recognition, text classification, natural language understanding, and natural language generation.

Computational semantics is a subfield of computational linguistics. Its goal is to elucidate the cognitive mechanisms supporting the generation and interpretation of meaning in humans. It usually involves the creation of computational models that simulate particular semantic phenomena, and the evaluation of those models against data from human participants. While computational semantics is a scientific field, it has many applications in real-world settings and substantially overlaps with Artificial Intelligence.

Broadly speaking, the discipline can be subdivided into areas that mirror the internal organization of linguistics. For example, lexical semantics and frame semantics have active research communities within computational linguistics. Some popular methodologies are also strongly inspired by traditional linguistics. Most prominently, the area of distributional semantics, which underpins investigations into embeddings and the internals of Large Language Models, has roots in the work of Zellig Harris.

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.

Speech recognition applications include voice user interfaces, where the user speaks to a device, which "listens" and processes the audio. Common voice applications include interpreting commands for calling, call routing, home automation, and aircraft control. These applications are called direct voice input. Productivity applications include searching audio recordings, creating transcripts, and dictation.

A phraseme, also called a set phrase, fixed expression, multiword expression (in computational linguistics), or more specifically idiom, is a multi-word or multi-morphemic utterance whose components include at least one that is selectionally constrained or restricted by linguistic convention such that it is not freely chosen.

In the most extreme cases, there are expressions such as kicked the bucket (to express with flippancy that a person has died) where the unit is selected as a whole to express a meaning that bears little or no relation to the meanings of its parts. Someone unfamiliar with the expression would not know its meaning.

In linguistics, anaphora (/əˈnæfərə/) is the use of an expression whose interpretation depends upon another expression in context (its antecedent). In a narrower sense, anaphora is the use of an expression that depends specifically upon an antecedent expression and thus is contrasted with cataphora, which is the use of an expression that depends upon a postcedent expression. The anaphoric (referring) term is called an anaphor. For example, in the sentence Sally arrived, but nobody saw her, the pronoun her is an anaphor, referring back to the antecedent Sally. In the sentence Before her arrival, nobody saw Sally, the pronoun her refers forward to the postcedent Sally, so her is now a cataphor (and an anaphor in the broader sense, but not in a narrower one). Usually, an anaphoric expression is a pro-form or some other kind of deictic (contextually dependent) expression. Both anaphora and cataphora are species of endophora, referring to something mentioned elsewhere in a dialog or text.

Anaphora is an important concept for different reasons and on different levels: first, anaphora indicates how discourse is constructed and maintained; second, anaphora binds different syntactical elements together at the level of the sentence; third, anaphora presents a challenge to natural language processing in computational linguistics, since the identification of the reference can be difficult; and fourth, anaphora partially reveals how language is understood and processed, which is relevant to fields of linguistics interested in cognitive psychology.

Natural-language user interface (LUI or NLUI) is a type of computer human interface where linguistic phenomena such as verbs, phrases and clauses act as UI controls for creating, selecting and modifying data in software applications.

In interface design, natural-language interfaces are sought after for their speed and ease of use, but most suffer the challenges to understanding wide varieties of ambiguous input.Natural-language interfaces are an active area of study in the field of natural-language processing and computational linguistics. An intuitive general natural-language interface is one of the active goals of the Semantic Web.

In computational linguistics and computer science, edit distance is a string metric, i.e. a way of quantifying how dissimilar two strings (e.g., words) are to one another, that is measured by counting the minimum number of operations required to transform one string into the other. Edit distances find applications in natural language processing, where automatic spelling correction can determine candidate corrections for a misspelled word by selecting words from a dictionary that have a low distance to the word in question. In bioinformatics, it can be used to quantify the similarity of DNA sequences, which can be viewed as strings of the letters A, C, G and T.

Different definitions of an edit distance use different sets of like operations. Levenshtein distance operations are the removal, insertion, or substitution of a character in the string. Being the most common metric, the term Levenshtein distance is often used interchangeably with edit distance.