Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6857248 | Information Sciences | 2016 | 23 Pages |
Abstract
Aspect-phrase grouping is an important task for aspect finding in sentiment analysis. Most existing methods for this task are based on a window-context model, which assumes that the same aspect has similar co-occurrence contexts. This model does not always work well in practice. In this paper, we develop a novel weighted context representation model based on semantic relevance, which exploits word embedding method to represent aspect-phrase. And we encode the lexical knowledge as constraints with a degree of belief, and further propose a flexible-constrained K-means algorithm to cluster aspect-phrases. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Shufeng Xiong, Donghong Ji,