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Part of
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. 174–197

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

| University of Granada
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.
Keywords: named river, predicate-argument structure analysis, semantic annotation, three-component multi-word term, bracketing prediction, terminological knowledge base, frame-based terminology
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
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References (30)
References
Barrière, C., & Ménard, P. A. (2014). Multiword noun compound bracketing using Wikipedia. In Proceedings of the First Workshop on Computational Approaches to Compound Analysis (pp. 72–80). ACL. DOI logoGoogle Scholar
Bergsma, S., Pitler, E., & Lin, D. (2010). Creating robust supervised classifiers via Web-scale n-gram data. In Proceedings of the 48th Annual Meeting of the ACL (pp. 865–874). ACL.Google Scholar
Boas, H. C. (2005). Semantic frames as interlingual representations for multilingual lexical databases. International Journal of Lexicography, 18(4), 445–478. DOI logoGoogle Scholar
Buendía-Castro, M., & Faber, P. (2016). Phraseological correspondence in English and Spanish specialized texts. In G. Corpas (Ed.), Computerised and corpus – based approaches to phraseology: Monolingual and multilingual perspectives (pp. 391–398). Tradulex.Google Scholar
Faber, P. (2009). The cognitive shift in terminology and specialized translation. MonTI. Monografías de Traducción e Interpretación [Monographs on Translation and Interpreting], 1, 107–134. DOI logoGoogle Scholar
(Ed.). (2012). A cognitive linguistics view of terminology and specialized language. De Gruyter Mouton. DOI logoGoogle Scholar
Faber, P., & Cabezas – García, M. (2019). Specialized knowledge representation: From terms to frames. Research in Language, 17(2), 197–211. DOI logoGoogle Scholar
Faber, P., & Mairal, R. (1999). Constructing a lexicon of English verbs. Mouton de Gruyter. DOI logoGoogle Scholar
Faber, P., León-Araúz, P., & Prieto, J. A. (2009). Semantic relations, dynamicity, and terminological knowledge bases. Current Issues in Language Studies, 1, 1–23.Google Scholar
Faruqui, M., & Dyer, C. (2015). Non-distributional word vector representations. In Proceedings of the 53rd Annual Meeting of the ACL (pp. 464–469). ACL. DOI logoGoogle Scholar
Fillmore, C. (1968). The case for case. In E. Bach & R. Harms (Eds.), Universals in Linguistic Theory (pp. 1–89). Holt, Rinehart, and Winston.Google Scholar
Gil-Berrozpe J. C, León-Araúz, P., & Faber, P. (2019). Ontological knowledge enhancement in EcoLexicon. In Electronic Lexicography in the 21st Century. Proceedings of the eLex 2019 Conference (pp. 177–197). Lexical Computing.
Girju, R., Moldovan, D. I., Tatu, M., & Antohe, D. (2005). On the semantics of noun compounds. Computer Speech and Language, 19(4), 479–496. DOI logoGoogle Scholar
Green, N. (2011). Effects of noun phrase bracketing in dependency parsing and machine translation. In 49th Annual Meeting of the ACL (pp. 69–74). ACL.Google Scholar
Kim, S. N., & Baldwin, T. (2013). A lexical semantic approach to interpreting and bracketing English noun compounds. Natural Language Engineering, 19(3), 385–407. DOI logoGoogle Scholar
Kroeger, P. R. (2005). Analyzing grammar: An introduction. Cambridge University Press. DOI logoGoogle Scholar
Lauer, M. (1994). Conceptual association for compound noun analysis. In Proceedings of the Student Session at the 32nd Annual Meeting of the Association for Computational Linguistics (pp. 337–339). CoRR. DOI logoGoogle Scholar
(1995). Corpus statistics meet the noun compound: Some empirical results. In Proceedings of the 3rd Annual Meeting of the ACL (pp. 47–54). ACL. DOI logoGoogle Scholar
Lazaridou, A., Vecchi, E. M., & Baroni, M. (2013). Fish transporters and miracle homes: How compositional distributional semantics can help NP parsing. In Proceedings of the 2013 Conference on Empirical Methods in NLP (pp. 1908–1913). ACL.
León-Araúz, P., Cabezas-García, M., & Faber, P. (2021). Multiword-term bracketing and representation in terminological knowledge bases. In Proceedings of the eLex 2021 Conference (pp. 139–163). Lexical Computing CZ.
León-Araúz, P., San Martín, A., & Reimerink, A. (2018). The EcoLexicon English corpus as an open corpus in Sketch Engine. In Proceedings of the 18th EURALEX International Congress (pp. 893–901). Euralex.Google Scholar
Marcus, M. (1980). A theory of syntactic recognition for natural language. MIT Press.Google Scholar
Ménard, P. A., & Barrière, C. (2014). Linked open data and web corpus data for noun compound bracketing. In Proceedings of the 9th International Conference on Language Resources and Evaluation (pp. 702–709). ELRA.
Nakov, P., & Hearst, M. (2005). Search engine statistics beyond the n-gram: Application to noun compound bracketing. In Proceedings of the 9th Conference on Computational Natural Language Learning (pp. 17–24). ACL. DOI logo
Pimentel, J. (2015). Using frame semantics to build a bilingual lexical resource on legal terminology. In H. J. Kockaert & F. Steurs (Eds.), Handbook of Terminology (Vol. 1, pp. 425–450). John Benjamins. DOI logoGoogle Scholar
Pitler, E., Bergsma, S., Lin, D., & Church, K. W. (2010). Using web-scale n-grams to improve base NP parsing performance. In Proceedings of the 23rd International Conference on Computational Linguistics (pp. 886–894). ACL.
Resnik, P. S. (1993). Selection and information: A class-based approach to lexical relationships [Doctoral dissertation, University of Pennsylvania].
Thompson, P., Iqbal, S. A., McNaught, J., & Ananiadou, S. (2009). Construction of an annotated corpus to support biomedical information extraction. BMC Bioinformatics, 10, 349. DOI logoGoogle Scholar
Vadas, D., & Curran, J. R. (2007). Large-scale supervised models for noun phrase bracketing. In Proceedings of the 10th Conference of the Pacific ACL (pp. 104–112). ACL.
(2008). Parsing noun phrase structure with CCG. In Proceedings of the 46th Annual Meeting of the ACL (pp. 335–343). ACL.Google Scholar