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