"Computational semantics is the study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions."
The study of how to represent and process meaning in natural language using formal logic and computer algorithms.
Natural Language Processing (NLP): NLP is the field of linguistics that deals with the interaction between computers and human languages. It encompasses a wide range of topics, including syntax, morphology, semantics, and pragmatics.
Machine Learning: Machine learning is a subset of artificial intelligence that focuses on computers being able to learn from experience and improve their performance over time. It is a vital part of computational semantics since it provides the foundation for many applications.
Corpus Linguistics: Corpus linguistics is the study of language based on large collections of text, known as corpora. It involves the creation, annotation, and analysis of corpora, and can provide valuable insights into the structure and usage of language.
Semantics: Semantics is the study of meaning in language. It involves examining the relationships between words, phrases, and sentences to reveal underlying patterns and structures.
Syntactics: Syntactics is the study of the structure and organization of language. It involves analyzing the relationships between words, phrases, and sentences to reveal patterns and rules.
Pragmatics: Pragmatics is the study of how context affects the meaning of language. It involves examining the social and cultural factors that influence the use of language, as well as the roles that speakers and listeners play in communication.
Ontology: Ontology is the study of the nature of existence, and in computational semantics, it involves the creation of structured frameworks for representing knowledge and information.
Information Extraction: Information extraction is the practice of automatically extracting information from textual data. It involves identifying key entities and concepts and associating them with specific pieces of information.
Sentiment Analysis: Sentiment analysis is the practice of automatically determining the emotional tone of a piece of text. It can be useful for analyzing consumer feedback, social media data, and other forms of user-generated content.
Named Entity Recognition (NER): Named Entity Recognition is the practice of automatically identifying and categorizing entities such as names, dates, and locations in text. It can be useful for organizing and analyzing large amounts of textual data.
Lexical Semantics: This branch of computational semantics deals with the analysis of semantics at the level of individual words or word meanings, and building a relationship between these meanings.
Formal Semantics: Formal semantics deals with the representation of natural language meaning using formal logic systems. It involves the use of formal languages to model the meaning of words and sentences, and derive logical inferences from them.
Distributional Semantics: This approach involves the statistical analysis of co-occurrence patterns of words in large corpora to derive their meaning. It focuses on the relationships between words and the contexts in which they appear, and has been successful in developing techniques for applications such as word sense disambiguation and sentiment analysis.
Compositional Semantics: This branch of computational semantics aims to build meaning representations of larger linguistic units such as phrases, sentences, and discourse by combining the meanings of their constituent parts.
Pragmatics: Pragmatics involves the study of language use in context, rather than just the meaning of words and sentences themselves. It deals with implicatures, presuppositions, and the effects of context on meaning, among other things.
Ontology: Ontology involves the creation of semantic structures, or formal models, that represent concepts or objects of interest, and the relationships between them. Ontologies are important for representing knowledge about a particular domain, and have applications in natural language understanding and machine reasoning.
Cross-Lingual Semantics: This subfield focuses on analyzing and comparing the meaning of words and sentences across different languages, with the aim of developing systems for accurate machine translation, cross-lingual information retrieval, and cross-lingual natural language processing.
Dialogue Modeling: This approach involves the development of models of human-human or human-computer dialogue, with the goal of building more natural and effective communication systems. It involves the study of language use in conversation and the development of dialogue management systems, among other things.
Machine Translation: This field deals with the development of systems that can automatically translate text from one language to another. It involves the use of statistical and machine learning techniques, as well as rule-based methods, to create translation models.
Sentiment Analysis: This subfield deals with the analysis of opinions, emotions, and attitudes expressed in natural language text. It involves the use of techniques from natural language processing, machine learning, and statistics to identify and classify sentiment in text.
"It consequently plays an important role in natural-language processing and computational linguistics."
"Some traditional topics of interest are: construction of meaning representations, semantic underspecification, anaphora resolution, presupposition projection, and quantifier scope resolution."
"Methods employed usually draw from formal semantics or statistical semantics."
"Computational semantics has points of contact with the areas of lexical semantics (word-sense disambiguation and semantic role labeling)."
"Computational semantics has points of contact with the areas of discourse semantics."
"Computational semantics has points of contact with the areas of knowledge representation and automated reasoning (in particular, automated theorem proving)."
"Since 1999 there has been an ACL special interest group on computational semantics, SIGSEM."