Graph-based Chinese word sense disambiguation with multi-knowledge integration

Wenpeng Lu*, Fanqing Meng, Shoujin Wang, Guoqiang Zhang, Xu Zhang, Antai Ouyang, Xiaodong Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
230 Downloads (Pure)


Word sense disambiguation (WSD) is a fundamental but significant task in natural language processing, which directly affects the performance of upper applications. However, WSD is very challenging due to the problem of knowledge bottleneck, i.e., it is hard to acquire abundant disambiguation knowledge, especially in Chinese. To solve this problem, this paper proposes a graph-based Chinese WSD method with multi-knowledge integration. Particularly, a graph model combining various Chinese and English knowledge resources by word sense mapping is designed. Firstly, the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet. Then, English word similarity is computed based on English word embeddings and knowledge base. Chinese word similarity is evaluated with Chinese word embedding and HowNet, respectively. The weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities, which are utilized to construct a disambiguation graph. The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words. Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines.

Original languageEnglish
Pages (from-to)197-212
Number of pages16
JournalComputers, Materials and Continua
Issue number1
Publication statusPublished - 2019

Bibliographical note

Copyright the Author(s). Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • word sense disambiguation
  • graph model
  • multi-knowledge integration
  • word similarity


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