Combining word embedding and semantic lexicon for Chinese word similarity computation

Abstract

Large corpus-based embedding methods have received increasing attention for their flexibility and effectiveness in many NLP tasks including Word Similarity (WS). However, these approaches rely on high-quality corpora and neglect the human’s intelligence contained in semantic resources such as Tongyici Cilin and Hownet. This paper proposes a novel framework for measuring the Chinese word similarity by combining word embedding and Tongyici Cilin. We also utilize retrieval techniques to extend the contexts of word pairs and calculate the similarity scores to weakly supervise the selection of a better result. In the Chinese Lexical Similarity Computation (CLSC) shared task, we rank No. 2 with the result of 0.457/0.455 of Spearman/Pearson rank correlation coefficient. After the submission, we boost the embedding model by merging an English model into the Chinese one and learning the co-occurrence sequence via LSTM networks. Our final results are 0.541/0.514, which outperform the state-of-the-art performance to the best of our knowledge.