Abstract
In this paper we present a new individual measure for the task of evocation strength prediction. The proposed solution is based on Dijkstra’s distances calculated on the WordNet graph expanded with polysemy relations. The polysemy network was constructed using chaining procedure executed on individual word senses of polysemous lemmas. We show that the shape of polysemy associations between WordNet senses has a positive impact on evocation strength prediction and the measure itself could be successfully reused in more complex ML frameworks.
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Notes
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WordNet glosses were semi-automatically interlinked with contextually appropriate synsets, https://wordnetcode.princeton.edu/glosstag.shtml.
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The experimental part was conducted in WEKA framework [8].
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Published by Centre for Translation Studies, University of Leeds: http://corpus.leeds.ac.uk/list.html, CC-BY licence.
- 8.
Shapiro-Wilk tests gave p-values equal to 0.4804 (NN) and 0.4923 (RF).
References
Barque, L., Chaumartin, F.R.: Regular polysemy in WordNet. J. Lang. Technol. Comput. Linguist. 24(2), 5–18 (2009)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
Boyd-Graber, J., Fellbaum, C., Osherson, D., Schapire, R.: Adding dense, weighted, connections to WordNet. In: Proceedings of the Global WordNet Conference (2006). docs/jbg-jeju.pdf
Cattle, A., Ma, X.: Predicting word association strengths. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1283–1288 (2017)
Chklovski, T., Mihalcea, R.: Building a sense tagged corpus with open mind word expert. In: Proceedings of the ACL-02 Workshop on Word Sense Disambiguation: Recent Successes and Future Directions, vol. 8, pp. 116–122. Association for Computational Linguistics (2002)
Cramer, I.: How well do semantic relatedness measures perform?: A meta-study. In: Proceedings of the 2008 Conference on Semantics in Text Processing, pp. 59–70. Association for Computational Linguistics (2008)
Fellbaum, C.: WordNet: An Electronic Lexical Database. The MIT Press, Cambridge (1998)
Frank, E., Hall, M., Witten, I.: The WEKA Workbench. Online Appendix for “Data Mining: Practical machine Learning Tools and Techniques”. Morgan Kaufmann, Cambridge (2016)
Freihat, A.A., Giunchiglia, F., Dutta, B.: A taxonomic classification of WordNet polysemy types. In: Proceedings of the 8th GWC Global WordNet Conference (2016)
Geeraerts, D.: Theories of Lexical Semantics. Oxford University Press, New York (2010)
Hayashi, Y.: Predicting the evocation relation between lexicalized concepts. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1657–1668 (2016)
Kacmajor, M., Kelleher, J.D.: Capturing and measuring thematic relatedness. Lang. Resour. Eval. 54(3), 645–682 (2019). https://doi.org/10.1007/s10579-019-09452-w
Lipka, L.: An Outline of English Lexicology: Lexical Structure, Word Semantics, and Word-formation, vol. 3. Walter de Gruyter, Berlin (2010)
Lyons, J.: Semantics, vol. 2. Cambridge University Press, Cambridge (1977)
Nikolova, S.S., Boyd-Graber, J., Fellbaum, C., Cook, P.: Better vocabularies for assistive communication aids: connecting terms using semantic networks and untrained annotators. In: ACM Conference on Computers and Accessibility (2009). docs/evocation-viva.pdf
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: EMNLP (2014)
Ramiro, C., Srinivasan, M., Malt, B.C., Xu, Y.: Algorithms in the historicalemergence of word senses. Proc. Natl. Acad. Sci. 115(10), 2323–2328 (2018). https://doi.org/10.1073/pnas.1714730115, https://www.pnas.org/content/115/10/2323
Rothe, S., Schütze, H.: Autoextend: extending word embeddings to embeddings for synsets and lexemes. arXiv preprint arXiv:1507.01127 (2015)
Youn, H., et al.: On the universal structure of human lexical semantics. Proc. Natl. Acad. Sci. 113(7), 1766–1771 (2016)
Acknowledgments
This research was financed by the National Science Centre, Poland, grant number 2018/29/B/HS2/02919, and supported by the Polish Ministry of Education and Science, Project CLARIN-PL.
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Janz, A., Maziarz, M. (2020). Chaining Polysemous Senses for Evocation Recognition. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_62
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