Year: 2018
Type: preprint
Abstract: 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 the cosines of 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2883449148