Modeling Semantic Compositionality with Sememe Knowledge
Fanchao Qi, Junjie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu, Maosong Sun
Abstract
Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememe-incorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC.All the code and data of this paper can be obtained on https://github.com/thunlp/Sememe-SC.- Anthology ID:
- P19-1571
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5706–5715
- Language:
- URL:
- https://aclanthology.org/P19-1571
- DOI:
- 10.18653/v1/P19-1571
- Cite (ACL):
- Fanchao Qi, Junjie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu, and Maosong Sun. 2019. Modeling Semantic Compositionality with Sememe Knowledge. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5706–5715, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Semantic Compositionality with Sememe Knowledge (Qi et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/P19-1571.pdf
- Code
- thunlp/Sememe-SC
- Data
- COS960