1. Exploiting flexible-constrained K-means clustering with word embedding for aspect-phrase grouping.
- Author
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Xiong, Shufeng and Ji, Donghong
- Subjects
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CONSTRAINT satisfaction , *K-means clustering , *EMBEDDINGS (Mathematics) , *GROUP theory , *COMPUTER algorithms - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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