Weighted Combinations of Ontological Features and Keywords for Document Clustering
Corressponding author's email:
tapchikhgkdt@hcmute.edu.vnKeywords:
named entity, latent semantics, clustering qualityAbstract
Keyword-based information processing has limitations due to simple treatment of words. In this paper, we introduce named entities as objectives into document clustering, which are the key elements defining document semantics and in many cases are of user concerns. First, the traditional keyword-based vector space model is adapted with vectors defined over spaces of entity names, types, name-type pairs, and identifiers, instead of keywords. Then, hierarchical document clustering can be performed using the similarity measure defined as a distance between the vectors representing documents. Experimental results are presented and discussed. Clustering documents by information of named entities could be useful for managing web-based learning materials with respect to related objects.
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