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Article In:
Approaches to Machine Translation
Edited by Mahdieh Fakhar, Monica Vilhelm and Paz Díez-Arcón
[Translation and Translanguaging in Multilingual Contexts 11:1] 2025
► pp. 5–30

Powerful variables for knowledge representation and bracketing prediction

| University of Granada
The acquisition of knowledge is essential for specialized translation, and the representation of specialized phraseology in terminological knowledge bases facilitates this process. The aim of this study is two-fold. Firstly, it describes how the semantic annotation of the predicate-argument structure of sentences mentioning named rivers can be addressed from the perspective of Frame-based Terminology. The results show that this approach, including the semantic variables of verb lexical domain, semantic role, and semantic category, provides valuable insights into the knowledge structures underlying the usage of named rivers in specialized texts. Secondly, this study explores whether the bracketing of a three-component multiword term can be predicted from the semantic information encoded in the sentence where the ternary compound and a named river are used as arguments. The semantic variables of lexical domain, semantic role, and semantic category allowed us to construct two machine-learning models capable of accurately predicting ternary-compound bracketing.
Keywords: predicate-argument structure analysis, semantic annotation, three-component multiword term, bracketing prediction, terminological knowledge base, Frame-based Terminology, named river
Article outline
  • 1.Introduction
  • 2.Frame-based Terminology
  • 3.Materials and methods
    • 3.1Corpus data
    • 3.2GeoNames geographic database
    • 3.3Recognition of named rivers
    • 3.4From multiword-term level to phrase level: Semantic annotation of predicate-argument structures for named rivers
      • 3.4.1Predicate classification in lexical domains
      • 3.4.2Semantic roles
      • 3.4.3Semantic categories
      • 3.4.4Semantic relations
      • 3.4.5Inter-annotator agreement
  • 4.Results of the semantic annotations
    • 4.1Lexical domain of action
    • 4.2Construction of frames evoked by named rivers
  • 5.Prediction of the bracketing of three-component multiword terms
    • 5.1Bracketing of multiword terms
    • 5.2Methods for bracketing prediction in the literature
    • 5.3Semantic approach to the prediction of ternary-compound bracketing
      • 5.3.1Description of the sample of ternary compounds
      • 5.3.2Supervised models
      • 5.3.3Data splitting
      • 5.3.4Model performance measures
      • 5.3.5Construction of the supervised models
    • 5.4Comparison of the results with previous research
  • 6.Conclusions
  • Notes
  • Author queries
  • References
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