List of Natural Language Processing Toolkits - Related Fields

Related Fields

Natural language processing contributes to, and makes use of (the theories, tools, and methodologies from), the following fields:

  • Automated reasoning – area of computer science and mathematical logic dedicated to understanding various aspects of reasoning, and producing software which allows computers to reason completely, or nearly completely, automatically. A sub-field of artificial intelligence, automatic reasoning is also grounded in theoretical computer science and philosophy of mind.
  • Linguistics – scientific study of human language. Natural language processing requires understanding of the structure and application of language, and therefore it draws heavily from linguistics.
    • Applied linguistics – interdisciplinary field of study that identifies, investigates, and offers solutions to language-related real-life problems. Some of the academic fields related to applied linguistics are education, linguistics, psychology, computer science, anthropology, and sociology. Some of the subfields of applied linguistics relevant to natural language processing are:
      • Bilingualism / Multilingualism –
      • Computer-mediated communication (CMC) – any communicative transaction that occurs through the use of two or more networked computers. Research on CMC focuses largely on the social effects of different computer-supported communication technologies. Many recent studies involve Internet-based social networking supported by social software.
      • Contrastive linguistics – practice-oriented linguistic approach that seeks to describe the differences and similarities between a pair of languages.
      • Conversation analysis (CA) – approach to the study of social interaction, embracing both verbal and non-verbal conduct, in situations of everyday life. Turn-taking is one aspect of language use that is studied by CA.
      • Discourse analysis – various approaches to analyzing written, vocal, or sign language use or any significant semiotic event.
      • Forensic linguistics – application of linguistic knowledge, methods and insights to the forensic context of law, language, crime investigation, trial, and judicial procedure.
      • Interlinguistics – study of improving communications between people of different first languages with the use of ethnic and auxiliary languages (lingua franca). For instance by use of intentional international auxiliary languages, such as Esperanto or Interlingua, or spontaneous interlanguages known as pidgin languages.
      • Language assessment – assessment of first, second or other language in the school, college, or university context; assessment of language use in the workplace; and assessment of language in the immigration, citizenship, and asylum contexts. The assessment may include analyses of listening, speaking, reading, writing or cultural understanding, with respect to understanding how the language works theoretically and the ability to use the language practically.
      • Language pedagogy – science and art of language education, including approaches and methods of language teaching and study. Natural language processing is used in programs designed to teach language, including first and second language training.
      • Language planning –
      • Language policy –
      • Lexicography –
      • Literacies –
      • Pragmatics –
      • Second language acquisition –
      • Stylistics –
      • Translation –
    • Computational linguistics – interdisciplinary field dealing with the statistical or rule-based modeling of natural language from a computational perspective. The models and tools of computational linguistics are used extensively in the field of natural language processing, and vice versa.
      • Computational semantics –
      • Corpus linguistics – study of language as expressed in samples (corpora) of "real world" text. Corpora is the plural of corpus, and a corpus is a specifically selected collection of texts (or speech segments) composed of natural language. After it is constructed (gathered or composed), a corpus is analyzed with the methods of computational linguistics to infer the meaning and context of its components (words, phrases, and sentences), and the relationships between them. Optionally, a corpus can be annotated ("tagged") with data (manually or automatically) to make the corpus easier to understand (e.g., part-of-speech tagging). This data is then applied to make sense of user input, for example, to make better (automated) guesses of what people are talking about or saying, perhaps to achieve more narrowly focused web searches, or for speech recognition.
    • Metalinguistics –
    • Sign linguistics – scientific study and analysis of natural sign languages, their features, their structure (phonology, morphology, syntax, and semantics), their acquisition (as a primary or secondary language), how they develop independently of other languages, their application in communication, their relationships to other languages (including spoken languages), and many other aspects.
  • Human–computer interaction – the intersection of computer science and behavioral sciences, this field involves the study, planning, and design of the interaction between people (users) and computers. Attention to human-machine interaction is important, because poorly designed human-machine interfaces can lead to many unexpected problems. A classic example of this is the Three Mile Island accident where investigations concluded that the design of the human–machine interface was at least partially responsible for the disaster.
  • Information retrieval (IR) – field concerned with storing, searching and retrieving information. It is a separate field within computer science (closer to databases), but IR relies on some NLP methods (for example, stemming). Some current research and applications seek to bridge the gap between IR and NLP.
  • Knowledge representation (KR) – area of artificial intelligence research aimed at representing knowledge in symbols to facilitate inferencing from those knowledge elements, creating new elements of knowledge. Knowledge Representation research involves analysis of how to reason accurately and effectively and how best to use a set of symbols to represent a set of facts within a knowledge domain.
    • Semantic network –
      • Semantic Web –
  • Machine learning –
    • Pattern recognition –
    • Statistical classification –

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