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In:Handbook of Terminology: Volume 3. Legal Terminology
Edited by Łucja Biel and Hendrik J. Kockaert
[Handbook of Terminology 3] 2023
► pp. 511–541

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Automatic term recognition and legal language

A shorter path to the lexical profiling of legal texts?

 | Universidad de Murcia
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Available under the Creative Commons Attribution-NoDerivatives (CC BY-ND) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 1 December 2023
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https://doi.org/10.1075/hot.3.aut2
Abstract
Natural Language Processing (NLP) tools offer language scholars a wide array of possibilities to examine, amongst other, the lexicon in any text collection. This research was designed as an attempt to try to measure the degree of precision of three of these methods (Chung 2003; Drouin 2003; Scott 2008a) through their implementation on two corpora of Spanish and British judicial decisions which revolve around the topic of immigration. In addition, the last section of this chapter explores the lexical inventories extracted by each method (the top 500 candidate terms (CTs) in each case) by grouping them into ad hoc thematic categories, the most numerous being, as was to be expected, legal terms, followed by territory, evaluative items, crime and family.
Article outline
  • 1.Introduction
  • 2.ATR and legal language
  • 3.Methodology
    • 3.1Method description
      • 3.1.1Keywords
      • 3.1.2TermoStat
      • 3.1.3Chung
    • 3.2Corpus description
    • 3.3Method implementation
  • 4.Results and discussion
    • 4.1Method validation
    • 4.2Thematic term categories
      • 4.2.1Corpus-driven semantic classification
      • 4.2.2Semantic categorization using UMUTextStats
  • 5.Conclusion
  • Notes
  • References
References (51)
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