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Cao, Guihong; Song, Dawei and Bruza, Peter
(2004).
DOI: https://doi.org/10.1007/978-3-540-24655-8_103
Abstract
One way of representing semantics is via a high dimensional conceptual space constructed from lexical co-occurrence. Concepts (words) are represented as a vector whereby the dimensions are other words. As the words are represented as dimensional objects, clustering techniques can be applied to compute word clusters. Conventional clustering algorithms, e.g., the K-means method, however, normally produce crisp clusters, i.e., an object is assigned to only one cluster. This is sometimes not desirable. Therefore, a fuzzy membership function can be applied to the K-Means clustering, which models the degree of an object belonging to certain cluster. This paper introduces a fuzzy k-means clustering algorithm and how it is used to word clustering on the high dimensional semantic space constructed by a cognitively motivated semantic space model, namely Hyperspace Analogue to Language. A case study demonstrates the method is promising.
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- Item ORO ID
- 9326
- Item Type
- Conference or Workshop Item
- ISBN
- 3-540-21371-6, 978-3-540-21371-0
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
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