Abstract
Aspect-phrase clustering is an important task for aspect finding in aspect-level sentiment analysis. Most of existing methods for this problem are based on a context model which aggregates related sentences that contains assigned aspect-phrase as context. In this paper, we explore a novel idea, capacity limitation, which states that the number of aggregated sentences in an aspect-phrase group has upper bound. And we propose a capacity constrained K-means algorithm to cluster aspect-phrases which encodes the capacity limitation as constraint. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods.
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Xiong, S., Ji, D. (2015). Exploiting Capacity-Constrained K-Means Clustering for Aspect-Phrase Grouping. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_34
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DOI: https://doi.org/10.1007/978-3-319-25159-2_34
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