Clustering Chinese Product Features with Multilevel Similarity

This paper presents an unsupervised hierarchical clustering approach for grouping co-referred features in Chinese product reviews. To handle different levels of connections between co-referred product features, we consider three similarity measures, namely the literal similarity, the word embedding-based semantic similarity and the explanatory evaluation based contextual similarity. We apply our approach to two corpora of product reviews in car and mobilephone domains. We demonstrate that combining multilevel similarity is of great value to feature normalization.