Abstract
We introduce a comprehensive evaluation benchmark for Polish Word Sense Disambiguation task. The benchmark consists of 7 distinct datasets with sense annotations based on plWordNet–4.2. As far as we know, our work is a first attempt to standardise existing sense annotated data for Polish. We also follow the recent trends of neural WSD solutions and we test transfer learning models, as well as hybrid architectures combining lexico-semantic networks with neural text encoders. Finally, we investigate the impact of bilingual training on WSD performance. The bilingual model obtains new State of the Art performance in Polish WSD task.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agirre, E., López de Lacalle, O., Soroa, A.: The risk of sub-optimal use of open source NLP software: UKB is inadvertently state-of-the-art in knowledge-based WSD. In: Proceedings of the Workshop for NLP Open Source Software (NLP-OSS), Melbourne, Australia (2018)
Bevilacqua, M., Navigli, R.: Breaking through the 80% glass ceiling: raising the state of the art in word sense disambiguation by incorporating knowledge graph information. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2854–2864 (2020)
Broda, B., Marcińczuk, M., Maziarz, M., Radziszewski, A., Wardyński, A.: KPWr: towards a free corpus of Polish. In: Proceedings of the 8th International Conference on Language Resources and Evaluation. Istanbul, Turkey, May 2012
Dziob, A., Piasecki, M., Rudnicka, E.K.: plWordNet 4.1 - a linguistically motivated, corpus-based bilingual resource. In: Proceedings of the 10th Global Wordnet Conference, pp. 353–362
Hajnicz, E.: Lexico-semantic annotation of składnica treebank by means of PLWN lexical units. In: Proceedings of the 7th Global Wordnet Conference, Tartu, Estonia, January 2014
Harabagiu, S., Moldovan, D.: Knowledge processing on an extended wordnet. WordNet Electron. Lexical Database 305, 381–405 (1998)
Janz, A., Chlebus, J., Dziob, A., Piasecki, M.: Results of the PolEval 2020 shared task 3: word sense disambiguation. In: Proceedings of the PolEval 2020 Workshop, p. 65
Janz, A., Kocon, J., Piasecki, M., Zasko-Zielinska, M.: plWordNet as a basis for large emotive lexicons of Polish. In: Proceedings of Human Language Technologies as a Challenge for Computer Science and Linguistics Poznan, pp. 189–193 (2017)
Janz, A., Piasecki, M.: Word sense disambiguation based on constrained random walks in linked semantic networks. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing, Varna, Bulgaria (2019)
Kędzia, P., Piasecki, M., Orlińska, M.: Word sense disambiguation based on large scale Polish CLARIN heterogeneous lexical resources. Cogn. Stud. (15) (2015)
Maziarz, M., Piasecki, M., Rudnicka, E., Szpakowicz, S., Kędzia, P.: plWordNet 3.0-a comprehensive lexical-semantic resource. In: Proceedings of COLING 2016, pp. 2259–2268 (2016)
Miller, G.A.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. Trans. Assoc. Comput. Ling. 2, 231–244 (2014)
Navigli, R., Ponzetto, S.P.: BabelNet: building a very large multilingual semantic network. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 216–225 (2010)
Pasini, T., Raganato, A., Navigli, R.: XL-WSD: an extra-large and cross-lingual evaluation framework for word sense disambiguation. In: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press (2021)
Pease, A.: Ontology - A Practical Guide. Articulate Software Press, Angwin (2011)
Ponzetto, S.P., Navigli, R.: Knowledge-rich word sense disambiguation rivaling supervised systems. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1522–1531 (2010)
Raganato, A., Camacho-Collados, J., Navigli, R.: 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, vol. 1. pp. 99–110 (2017)
Rudnicka, E., Maziarz, M., Piasecki, M., Szpakowicz, S.: A strategy of mapping polish wordnet onto Princeton wordnet. In: Proceedings of COLING 2012, pp. 1039–1048 (2012)
Tan, L., Bond, F.: Building and annotating the linguistically diverse NTU-MC (NTU-multilingual corpus). In: Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation, Singapore (2011)
Wang, Z., Lipton, Z.C., Tsvetkov, Y.: On negative interference in multilingual models: findings and a meta-learning treatment. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4438–4450 (2020)
Zaśko-Zielińska, M., Piasecki, M.: Towards emotive annotation in plWordNet 4.0. In: Proceedings of the 9th Global Wordnet Conference, pp. 153–162 (2018)
Acknowledgment
This work was co-financed by (1) the Polish Ministry of Education and Science, CLARIN-PL; (2) the European Regional Development Fund as a part of the 2014–2020 Smart Growth Operational Programme, project number POIR.04.02.00-00C002/19; and (3) by the National Science Centre, Poland, grant number 2018/29/B/HS2/02919.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Janz, A., Dziob, A., Oleksy, M., Baran, J. (2022). A Unified Sense Inventory for Word Sense Disambiguation in Polish. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_70
Download citation
DOI: https://doi.org/10.1007/978-3-031-08754-7_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08753-0
Online ISBN: 978-3-031-08754-7
eBook Packages: Computer ScienceComputer Science (R0)