Syntactic-Semantic Dependency Correlation in Semantic Role Labeling: A Shift in Semantic Label Distributions
Author:
Affiliation:
1. The University of Tokyo
Publisher
Association for Natural Language Processing
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://www.jstage.jst.go.jp/article/jnlp/29/3/29_1002/_pdf
Reference16 articles.
1. Cai, R. and Lapata, M. (2019). “Syntax-aware Semantic Role Labeling without Parsing.” Transactions of the Association for Computational Linguistics, 7, pp. 343–356.
2. Chen, J., He, X., and Miyao, Y. (2022). “Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models.” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7959–7969, Dublin, Ireland. Association for Computational Linguistics.
3. Dozat, T. and Manning, C. D. (2018). “Simpler but More Accurate Semantic Dependency Parsing.” In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 484–490, Melbourne, Australia. Association for Computational Linguistics.
4. Gildea, D. and Palmer, M. (2002). “The Necessity of Parsing for Predicate Argument Recognition.” In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 239–246, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics.
5. He, L., Lee, K., Lewis, M., and Zettlemoyer, L. (2017). “Deep Semantic Role Labeling: What Works and What’s Next.” In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 473–483, Vancouver, Canada. Association for Computational Linguistics.
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