Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Resolving Regular Polysemy in Named Entities (2401.09758v1)

Published 18 Jan 2024 in cs.CL

Abstract: Word sense disambiguation primarily addresses the lexical ambiguity of common words based on a predefined sense inventory. Conversely, proper names are usually considered to denote an ad-hoc real-world referent. Once the reference is decided, the ambiguity is purportedly resolved. However, proper names also exhibit ambiguities through appellativization, i.e., they act like common words and may denote different aspects of their referents. We proposed to address the ambiguities of proper names through the light of regular polysemy, which we formalized as dot objects. This paper introduces a combined word sense disambiguation (WSD) model for disambiguating common words against Chinese Wordnet (CWN) and proper names as dot objects. The model leverages the flexibility of a gloss-based model architecture, which takes advantage of the glosses and example sentences of CWN. We show that the model achieves competitive results on both common and proper nouns, even on a relatively sparse sense dataset. Aside from being a performant WSD tool, the model further facilitates the future development of the lexical resource.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (66)
  1. Eneko Agirre and Philip Edmonds, editors. Word Sense Disambiguation. Springer Netherlands, 2006. doi: 10.1007/978-1-4020-4809-8.
  2. Roberto Navigli. Word sense disambiguation: A survey. ACM Computing Surveys, 41(2):1–69, feb 2009. doi: 10.1145/1459352.1459355.
  3. Recent trends in word sense disambiguation: A survey. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, aug 2021. doi: 10.24963/ijcai.2021/593.
  4. With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2020a.
  5. Random walks for knowledge-based word sense disambiguation. Computational Linguistics, 40(1):57–84, March 2014. doi: 10.1162/COLI˙a˙00164. URL https://aclanthology.org/J14-1003.
  6. Sensembert: Context-enhanced sense embeddings for multilingual word sense disambiguation. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2020b.
  7. Representation Learning for Natural Language Processing. Springer Singapore, 2020. doi: 10.1007/978-981-15-5573-2.
  8. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26, 2013.
  9. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146, 2017. doi: 10.1162/tacl˙a˙00051. URL https://aclanthology.org/Q17-1010.
  10. Symbolic, distributed, and distributional representations for natural language processing in the era of deep learning: A survey. Frontiers in Robotics and AI, 6, 2020. ISSN 2296-9144. doi: 10.3389/frobt.2019.00153. URL https://www.frontiersin.org/article/10.3389/frobt.2019.00153.
  11. Analysing lexical semantic change with contextualised word representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3960–3973, Online, July 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.acl-main.365. URL https://aclanthology.org/2020.acl-main.365.
  12. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2227–2237, New Orleans, Louisiana, June 2018. Association for Computational Linguistics. doi: 10.18653/v1/N18-1202. URL https://aclanthology.org/N18-1202.
  13. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.
  14. BERT: Pre-training of deep bidirectional transformers for language understandingBERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, volume 1, pages 4171–4186, Minneapolis, Minnesota, 2019. doi: 10.18653/v1/N19-1423.
  15. Sensebert: Driving some sense into bert. arXiv preprint arXiv:1908.05646, 2019.
  16. Language modelling makes sense: Propagating representations through WordNet for full-coverage word sense disambiguation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5682–5691, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1569. URL https://aclanthology.org/P19-1569.
  17. Entity linking meets word sense disambiguation: a unified approach. Transactions of the Association for Computational Linguistics, 2:231–244, 2014. doi: 10.1162/tacl˙a˙00179. URL https://aclanthology.org/Q14-1019.
  18. Named entities for computational linguistics. John Wiley & Sons, 2016.
  19. A comparison of named-entity disambiguation and word sense disambiguation. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 860–867, Portorož, Slovenia, May 2016. European Language Resources Association (ELRA). URL https://aclanthology.org/L16-1139.
  20. Willy Van Langendonck. Theory and Typology of Proper Names. Gruyter, Walter de GmbH, August 2008. ISBN 3110197855. URL https://www.ebook.de/de/product/16110734/willy_van_langendonck_theory_and_typology_of_proper_names.html.
  21. Lena Peterson. Comments on two papers on the semantics of proper names. Names, 37(1):83–92, jun 1989. doi: 10.1179/nam.1989.37.1.83.
  22. Ronald Langacker. Active zones. Annual Meeting of the Berkeley Linguistics Society, 10:172, oct 1984. doi: 10.3765/bls.v10i0.3175.
  23. Polysemy: Current perspectives and approaches. Lingua, 157:1–16, apr 2015. doi: 10.1016/j.lingua.2015.02.002.
  24. Ju Apresjan. Regular polysemy. 1971.
  25. Nicholas Asher. Lexical meaning in context: A web of words. Cambridge University Press, 2011.
  26. Constructing chinese wordnet: Design principles and implementation. (in chinese). Zhong-Guo-Yu-Wen, 24:2:169–186, 2010.
  27. James Pustejovsky. The generative lexicon. MIT press, 1995.
  28. Modeling regular polysemy: A study on the semantic classification of Catalan adjectives. Computational Linguistics, 38(3):575–616, septemer 2012. doi: 10.1162/COLI˙a˙00093. URL https://aclanthology.org/J12-3005.
  29. Logical polysemy and subtyping. In New Frontiers in Artificial Intelligence, pages 17–24. Springer Berlin Heidelberg, 2013. doi: 10.1007/978-3-642-39931-2˙2.
  30. Agustin Vicente. Approaches to co-predication: Inherent polysemy and metaphysical relations. Journal of Pragmatics, 182:348–357, sep 2021. doi: 10.1016/j.pragma.2021.02.007.
  31. Geoffrey Nunberg. The non-uniqueness of semantic solutions: Polysemy. Linguistics and philosophy, pages 143–184, 1979.
  32. Semi-productive polysemy and sense extension. Journal of Semantics, 12(1):15–67, 1995. doi: 10.1093/jos/12.1.15.
  33. A plea for complex categories in ontologies. Applied Ontology, 10(3-4):285–296, dec 2015. doi: 10.3233/ao-150156.
  34. A word-embedding-based sense index for regular polysemy representation. In Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pages 70–78, Denver, Colorado, June 2015. Association for Computational Linguistics. doi: 10.3115/v1/W15-1510. URL https://aclanthology.org/W15-1510.
  35. Regular polysemy: from sense vectors to sense patterns. In Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V), pages 19–23, Osaka, Japan, December 2016. The COLING 2016 Organizing Committee. URL https://aclanthology.org/W16-5303.
  36. Logical metonymy in a distributional model of sentence comprehension. In Sixth Joint Conference on Lexical and Computational Semantics (* SEM 2017), pages 168–177, 2017.
  37. Patterns of polysemy and homonymy in contextualised language models. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2663–2676, Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.findings-emnlp.226. URL https://aclanthology.org/2021.findings-emnlp.226.
  38. Dominiek Sandra. What linguists can and can’t tell you about the human mind: A reply to croft. Cognitive linguistics, 9(4):361–378, 1998.
  39. The quality of lexical semantic resources: A survey. In Proceedings of The Fourth International Conference on Natural Language and Speech Processing (ICNLSP 2021), pages 117–129, Trento, Italy, 12–13 November 2021. Association for Computational Linguistics. URL https://aclanthology.org/2021.icnlsp-1.14.
  40. Word sense disambiguation: A unified evaluation framework and empirical comparison. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 99–110, Valencia, Spain, April 2017. Association for Computational Linguistics. URL https://aclanthology.org/E17-1010.
  41. Nibbling at the hard core of Word Sense Disambiguation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4724–4737, Dublin, Ireland, May 2022. Association for Computational Linguistics. doi: 10.18653/v1/2022.acl-long.324. URL https://aclanthology.org/2022.acl-long.324.
  42. Michael Lesk. Automatic sense disambiguation using machine readable dictionaries. In Proceedings of the 5th annual international conference on Systems documentation - SIGDOC '86. ACM Press, 1986. doi: 10.1145/318723.318728.
  43. Development and application of a metric on semantic nets. IEEE transactions on systems, man, and cybernetics, 19(1):17–30, 1989.
  44. Word sense disambiguation using conceptual density. In Proceedings of the 16th conference on Computational linguistics -. Association for Computational Linguistics, 1996. doi: 10.3115/992628.992635.
  45. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the 10th Research on Computational Linguistics International Conference, pages 19–33, Taipei, Taiwan, August 1997. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). URL https://aclanthology.org/O97-1002.
  46. Combining local context and wordnet similarity for word sense identification. WordNet: An electronic lexical database, 49(2):265–283, 1998.
  47. Lexical chains as representations of context for the detection and correction of malapropisms. WordNet: An electronic lexical database, 305:305–332, 1998.
  48. Improving word sense disambiguation in lexical chaining. In Proceedings of 18th International Joint Conference on Artificial Intelligence (IJCAI), pages 1486–1488, 2003.
  49. Pagerank on semantic networks, with application to word sense disambiguation. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics, pages 1126–1132, 2004.
  50. The stanford corenlp natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pages 55–60, 2014.
  51. It makes sense: A wide-coverage word sense disambiguation system for free text. In Proceedings of the ACL 2010 system demonstrations, pages 78–83, 2010.
  52. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar, October 2014. Association for Computational Linguistics. doi: 10.3115/v1/D14-1162. URL https://aclanthology.org/D14-1162.
  53. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, pages 160–167, 2008.
  54. Embeddings for word sense disambiguation: An evaluation study. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 897–907, 2016.
  55. A synset relation-enhanced framework with a try-again mechanism for word sense disambiguation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6229–6240, Online, November 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.emnlp-main.504. URL https://aclanthology.org/2020.emnlp-main.504.
  56. Moving down the long tail of word sense disambiguation with gloss informed bi-encoders. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1006–1017, Online, July 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.acl-main.95. URL https://aclanthology.org/2020.acl-main.95.
  57. GlossBERT: BERT for word sense disambiguation with gloss knowledge. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3509–3514, Hong Kong, China, November 2019. Association for Computational Linguistics. doi: 10.18653/v1/D19-1355. URL https://aclanthology.org/D19-1355.
  58. ESC: Redesigning WSD with extractive sense comprehension. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4661–4672, Online, June 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.naacl-main.371. URL https://aclanthology.org/2021.naacl-main.371.
  59. Making fine-grained and coarse-grained sense distinctions, both manually and automatically. Natural Language Engineering, 13(2):137–163, jul 2006. doi: 10.1017/s135132490500402x.
  60. Christiane Fellbaum. WordNet: An electronic lexical database. MIT press, 1998.
  61. Piek Vossen. A multilingual database with lexical semantic networks. Dordrecht: Kluwer Academic Publishers. doi, 10:978–94, 1998.
  62. Design of management system for Chinese corpus construction [in Chinese]. In Proceedings of Research on Computational Linguistics Conference XIV, pages 175–191, Tainan, Taiwan, August 2001. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). URL https://aclanthology.org/O01-1009.
  63. James Pustejovsky. A survey of dot objects. Technical report, Waltham, MA: Brandeis University, 2005.
  64. Semantic coercion in language: Beyond distributional analysis. Italian Journal of Linguistics, 20(1):175–208, 2008.
  65. Decoupled weight decay regularization. November 2017.
  66. Paul Buitelaar. CoreLex: Systematic Polysemy and Underspecification. PhD thesis, Brandeis University, 1998.
Citations (2)

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets