Incorporating Chinese Characters of Words for Lexical Sememe Prediction

Huiming Jin, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin


Abstract
Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words. To address this issue for Chinese, we propose a novel framework to take advantage of both internal character information and external context information of words. We experiment on HowNet, a Chinese sememe knowledge base, and demonstrate that our framework outperforms state-of-the-art baselines by a large margin, and maintains a robust performance even for low-frequency words.
Anthology ID:
P18-1227
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2439–2449
Language:
URL:
https://aclanthology.org/P18-1227
DOI:
10.18653/v1/P18-1227
Bibkey:
Cite (ACL):
Huiming Jin, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, and Leyu Lin. 2018. Incorporating Chinese Characters of Words for Lexical Sememe Prediction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2439–2449, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Incorporating Chinese Characters of Words for Lexical Sememe Prediction (Jin et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1227.pdf
Poster:
 P18-1227.Poster.pdf
Code
 thunlp/Character-enhanced-Sememe-Prediction