Word Sense Disambiguation Based on Graph and Knowledge Base
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-70451-3_3
Reference21 articles.
1. Luo, F., Liu, T., Xia, Q., Chang, B., Sui, Z.: Incorporating glosses into neural word sense disambiguation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2473–2482. Association for Computational Linguistics (2018)
2. Tripodi, R., Navigli, R.: Game theory meets embeddings: A unified framework for word sense disambiguation. 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), pp. 88–99. Association for Computational Linguistics (2019)
3. Blevins, T., Zettlemoyer, L.: 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, pp. 1006–1017. Association for Computational Linguistics (2020)
4. 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. Association for Computational Linguistics (2020)
5. Li, J.: Research on Chinese Word Sense Disambiguation Method. Tsinghua University, Beijing (1999)
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