Cross-lingual Lexical Sememe Prediction
Fanchao Qi, Yankai Lin, Maosong Sun, Hao Zhu, Ruobing Xie, Zhiyuan Liu
Abstract
Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe prediction, aiming to automatically predict sememes for words in other languages. We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. Experimental results on real-world datasets show that our proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingual sememe prediction. The codes and data of this paper are available at https://github.com/thunlp/CL-SP.- Anthology ID:
- D18-1033
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 358–368
- Language:
- URL:
- https://aclanthology.org/D18-1033
- DOI:
- 10.18653/v1/D18-1033
- Cite (ACL):
- Fanchao Qi, Yankai Lin, Maosong Sun, Hao Zhu, Ruobing Xie, and Zhiyuan Liu. 2018. Cross-lingual Lexical Sememe Prediction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 358–368, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual Lexical Sememe Prediction (Qi et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D18-1033.pdf
- Code
- thunlp/CL-SP