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In:Recent Advances in Multiword Units in Machine Translation and Translation Technology
Edited by Johanna Monti, Gloria Corpas Pastor, Ruslan Mitkov and Carlos Manuel Hidalgo-Ternero
[Current Issues in Linguistic Theory 366] 2024
► pp. 173–196

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Chapter 10
Semantic annotation of named rivers and its application for the prediction of multiword-term bracketing

 | University of Granada
Published online: 7 November 2024
DOI logo with link
https://doi.org/10.1075/cilt.366.10roj
Abstract
The acquisition of knowledge is essential for specialized translation, hence the representation of specialized phraseology in terminological knowledge bases is part of this process. The aim of this study was thus two-fold. Firstly, it describes how the semantic annotation of predicate-argument structure of sentences mentioning named rivers can be addressed from the perspective of Frame-based Terminology. The results showed that this approach 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 multi-word 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 annotations permitted construction of two machine-learning models capable of accurately predicting ternary-compound bracketing.
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 multi-word 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 movement
    • 4.2Construction of frames evoked by named rivers
  • 5.Prediction of the bracketing of three-component multi-word terms
    • 5.1Bracketing of multi-word terms
    • 5.2Methods for bracketing prediction in the literature
    • 5.3Semantic approach for 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
  • References
References (30)
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