Automatic Term Extraction

Heylen, Kris; Hertog, Dirk De

doi:10.1075/hot.1.aut1

Cited by 30 publications

(2 citation statements)

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“…Different approaches are used for (semi-)automatic term extraction: statistical, linguistic, and hybrid. The statistical approach applies statistical criteria to define the degree of termhood of candidate terms; the linguistic approach applies linguistic filtering techniques to identify specific syntactic term patterns (Heylen, and De Hertog 2015;Pazienza et al 2005). A prerequisite for the linguistic approach is a part of speech tagged corpus.…”

Section: Term Extractionmentioning

confidence: 99%

Multilingual legal terminology databases

Chiocchetti,

Lušicky,

Wissik

2023

Handbook of Terminology

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This chapter focuses on multilingual legal terminology databases (MLTDBs) and their specific features. We address the aspects influencing the structure of MLTDBs, i.e., the number of legal systems, the different usage scenarios, and target users. We describe the steps within a typical workflow for creating MLTDBs, i.e., needs analysis, design, documentation, term extraction, compilation of terminological entries, revision and quality assurance, maintenance, and dissemination, highlighting differences with other domains. We list the roles involved in legal terminology work, considering not only human users but also the increasing role played by machines as a new type of user of terminological data for various applications. Finally, we discuss aspects of quality management, planning, assurance, and control in MLTDBs, including relevant standards.

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Section: Term Extractionmentioning

confidence: 99%

Multilingual legal terminology databases

Chiocchetti,

Lušicky,

Wissik

2023

Handbook of Terminology

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“…Automatic Term Extraction (ATE) involves the extraction of technical terms from domain-specific corpora (Heylen and De Hertog, 2015;da Silva Conrado et al, 2014). ATE is a task of domain knowledge retrieval because the technical terms are used for lexicon update, ontology creation, summarization, named-entity recognition, etc.…”

Section: Introductionmentioning

confidence: 99%

Biomedical term extraction using fuzzy association

Das,

Majumder,

Phadikar

et al. 2023

Soft Comput

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Automatic term extraction from a biomedical text is a well-known problem in the area of natural language processing. It is carried out by employing four kinds of measures: linguistic and rule-based, dictionary-based, statistical, and machine learning. Automatic term extraction indicates whether or not two or more words come together in the text more often than by chance to form a biomedical term that automatically extracted using an automated system. A fuzzy set-theoretic approach is presented in this article that compares with the existing statistical measures. The experimental result shows that the fuzzy-measure offers better precision than the popular statistical-measures for extracting biomedical terms, especially when we have compared more than 60% ranked list of extracted terms.

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