1887
Volume 11, Issue 1
  • ISSN 2352-1805
  • E-ISSN: 2352-1813
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Abstract

Abstract

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.

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2025-01-07
2025-01-20
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References

  1. Barrière, Caroline, and Pierre A. Ménard
    2014 “Multiword Noun Compound Bracketing Using Wikipedia.” InProceedings of the First Workshop on Computational Approaches to Compound Analysis, 72–80. Dublin: ACL. https://aclanthology.org/W14-5708. 10.3115/v1/W14‑5708
    https://doi.org/10.3115/v1/W14-5708 [Google Scholar]
  2. Bergsma, Shane, Emily Pitler, and Dekang Lin
    2010 “Creating Robust Supervised Classifiers via Web-scale N-gram Data.” InProceedings of the 48th Annual Meeting of the ACL, 865–874. Uppsala, Sweden: ACL. https://aclanthology.org/P10-1089
    [Google Scholar]
  3. Boas, Hans C.
    2005 “Semantic Frames as Interlingual Representations for Multilingual Lexical Databases.” International Journal of Lexicography18 (4): 445–478. 10.1093/ijl/eci043
    https://doi.org/10.1093/ijl/eci043 [Google Scholar]
  4. Buendía-Castro, Míriam, and Pamela Faber
    2016 “Phraseological Correspondence in English and Spanish Specialized Texts.” InComputerised and Corpus-based Approaches to Phraseology: Monolingual and Multilingual Perspectives, ed. byGloria Corpas, 391–398. Geneva: Tradulex. https://shorturl.at/cnEF8
    [Google Scholar]
  5. Faber, Pamela
    2009 “The Cognitive Shift in Terminology and Specialized Translation.” MonTI. Monografías de Traducción e Interpretación [Monographs on Translation and Interpreting] 11: 107–134. 10.6035/MonTI.2009.1.5
    https://doi.org/10.6035/MonTI.2009.1.5 [Google Scholar]
  6. ed. 2012A Cognitive Linguistics View of Terminology and Specialized Language. Berlin: De Gruyter Mouton. 10.1515/9783110277203
    https://doi.org/10.1515/9783110277203 [Google Scholar]
  7. Faber, Pamela, and Melania Cabezas-García
    2019 “Specialized Knowledge Representation: From Terms to Frames.” Research in Language17 (2): 197–211. 10.2478/rela‑2019‑0012
    https://doi.org/10.2478/rela-2019-0012 [Google Scholar]
  8. Faber, Pamela, and Ricardo Mairal
    1999Constructing a Lexicon of English Verbs. Berlin: Mouton de Gruyter. 10.1515/9783110800623
    https://doi.org/10.1515/9783110800623 [Google Scholar]
  9. Faber, Pamela, Pilar León-Araúz, and Juan A. Prieto
    2009 “Semantic Relations, Dynamicity, and Terminological Knowledge Bases.” Current Issues in Language Studies11: 1–23. https://shorturl.at/irzFS
    [Google Scholar]
  10. Faruqui, Manaal, and Chris Dyer
    2015 “Non-distributional Word Vector Representations.” InProceedings of the 53rd Annual Meeting of the ACL, 464–469. Beijing: ACL. https://aclanthology.org/P15-2076/. 10.3115/v1/P15‑2076
    https://doi.org/10.3115/v1/P15-2076 [Google Scholar]
  11. Fillmore, Charles J.
    1968 “The Case for Case.” InUniversals in Linguistic Theory, ed. byEmmon Bach, and Robert Harms, 1–89. London: Holt, Rinehart, and Winston.
    [Google Scholar]
  12. Gil-Berrozpe, Juan C., Pilar León-Araúz, and Pamela Faber
    2019 “Ontological Knowledge Enhancement in EcoLexicon.” InElectronic Lexicography in the 21st Century. Proceedings of the eLex 2019 Conference, 177–197. Sintra: Lexical Computing. https://shorturl.at/qxKV8
    [Google Scholar]
  13. Girju, Roxana, Dan Moldovan, Marta Tatu, and Daniel Antohe
    2005 “On the Semantics of Noun Compounds.” Computer Speech and Language19 (4): 479–496. 10.1016/j.csl.2005.02.006
    https://doi.org/10.1016/j.csl.2005.02.006 [Google Scholar]
  14. Green, Nathan
    2011 “Effects of Noun Phrase Bracketing in Dependency Parsing and Machine Translation.” In49th Annual Meeting of the ACL, 69–74. Portland, OR: ACL. https://aclanthology.org/P11-3013/
    [Google Scholar]
  15. Kim, Su Nam, and Timothy Baldwin
    2013 “A Lexical Semantic Approach to Interpreting and Bracketing English Noun Compounds.” Natural Language Engineering19 (3): 385–407. 10.1017/S1351324913000107
    https://doi.org/10.1017/S1351324913000107 [Google Scholar]
  16. Klie, Jan-Christoph, Michael Bugert, Beto Boullosa, Richard Eckart de Castilho, and Iryna Gurevych
    2018 “The INCEpTION Platform: Machine-assisted and Knowledge-oriented Interactive Annotation” InProceedings of the 27th International Conference on Computational Linguistics, 5–9. Santa Fe, NM: ACL. https://aclanthology.org/C18-2002
    [Google Scholar]
  17. Kroeger, Paul R.
    2005Analyzing Grammar: An Introduction. New York, NY: Cambridge University Press. 10.1017/CBO9780511801679
    https://doi.org/10.1017/CBO9780511801679 [Google Scholar]
  18. Lauer, Mark
    1994 “Conceptual Association for Compound Noun Analysis.” InProceedings of the Student Session at the 32nd Annual Meeting of the ACL, 337–339. Las Cruces, NM: ACL. https://arxiv.org/abs/cmp-lg/9409002. 10.3115/981732.981785
    https://doi.org/10.3115/981732.981785 [Google Scholar]
  19. 1995 “Corpus Statistics Meet the Noun Compound: Some Empirical Results.” InProceedings of the 3rd Annual Meeting of the ACL, 47–54. Cambridge, MA: ACL. https://aclanthology.org/P95-1007. 10.3115/981658.981665
    https://doi.org/10.3115/981658.981665 [Google Scholar]
  20. Lazaridou, Angeliki, Eva M. Vecchi, and Marco Baroni
    2013 “Fish Transporters and Miracle Homes: How Compositional Distributional Semantics Can Help NP Parsing.” InProceedings of the 2013 Conference on Empirical Methods in NLP, 1908–1913. Seattle, WA: ACL. https://aclanthology.org/D13-1196
    [Google Scholar]
  21. León-Araúz, Pilar, Melania Cabezas-García, and Pamela Faber
    2021 “Multiword-term Bracketing and Representation in Terminological Knowledge Bases.” InProceedings of the eLex 2021 Conference, 139–163. Brno: Lexical Computing. https://shorturl.at/iEGP4
    [Google Scholar]
  22. León-Araúz, Pilar, Antonio San Martín, and Arianne Reimerink
    2018 “The EcoLexicon English Corpus as an Open Corpus in Sketch Engine.” InProceedings of the 18th EURALEX International Congress, 893–901. Ljubljana: Ljubljana University Press. https://shorturl.at/guILX
    [Google Scholar]
  23. Marcus, Mitchell P.
    1980A Theory of Syntactic Recognition for Natural Language. Cambridge, MA: The MIT Press.
    [Google Scholar]
  24. Ménard, Pierre A., and Caroline Barrière
    2014 “Linked Open Data and Web Corpus Data for Noun Compound Bracketing.” InProceedings of the 9th International Conference on Language Resources and Evaluation, 702–709. Reykjavik: ELRA. https://shorturl.at/wP019
    [Google Scholar]
  25. Nakov, Preslav, and Marti Hearst
    2005 “Search Engine Statistics beyond the N-gram: Application to Noun Compound Bracketing.” InProceedings of the 9th Conference on Computational Natural Language Learning, 17–24. Ann Arbor, MI: ACL. https://shorturl.at/wPTW5. 10.3115/1706543.1706547
    https://doi.org/10.3115/1706543.1706547 [Google Scholar]
  26. Pimentel, Janine
    2015 “Using Frame Semantics to Build a Bilingual Lexical Resource on Legal Terminology.” InHandbook of Terminology, ed. byHendrik J. Kockaert, and Frieda Steurs, 425–450. Amsterdam: John Benjamins. https://benjamins.com/online/hot/articles/usi1. 10.1075/hot.1.usi1
    https://doi.org/10.1075/hot.1.usi1 [Google Scholar]
  27. Pitler, Emely, Shane Bergsma, Dekang Lin, and Kenneth Church
    2010 “Using Web-scale N-grams to Improve Base NP Parsing Performance.” InProceedings of the 23rd International Conference on Computational Linguistics, 886–894. Beijing: ACL. https://aclanthology.org/C10-1100.pdf
    [Google Scholar]
  28. Resnik, Philip S.
    1993 Selection and Information: A Class-based Approach to Lexical Relationships. PhD diss. University of Pennsylvania. IRCS Technical Reports Series 200. Philadelphia, PA: University of Pennsylvania IRCS. https://shorturl.at/boK02
  29. Rojas-Garcia, Juan
    2022 “Semantic Representation of Context for Description of Named Rivers in a Terminological Knowledge Base.” Frontiers in Psychology131: 847024. 10.3389/fpsyg.2022.847024
    https://doi.org/10.3389/fpsyg.2022.847024 [Google Scholar]
  30. Thompson, Paul, Syed A. Iqbal, John McNaught, and Sophia Ananiadou
    2009 “Construction of an Annotated Corpus to Support Biomedical Information Extraction.” BMC Bioinformatics101: 349. 10.1186/1471‑2105‑10‑349
    https://doi.org/10.1186/1471-2105-10-349 [Google Scholar]
  31. Vadas, David, and James Curran
    2007 “Large-scale Supervised Models for Noun Phrase Bracketing.” InProceedings of the 10th Conference of the Pacific ACL, 104–112. Melbourne: ACL. https://shorturl.at/eptuX
    [Google Scholar]
  32. 2008 “Parsing Noun Phrase Structure with CCG.” InProceedings of the 46th Annual Meeting of the ACL, 335–343. Columbus, OH: ACL. https://aclanthology.org/P08-1039/
    [Google Scholar]
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