Search Result Clustering Using Informatively Named Entities
Hiroyuki Toda, Ryoji Kataoka, Masahiro Oku · International Journal of Human-Computer Interaction · 2007
Clustering the results of a search helps the user to review the information gathered. In this article, we regard the clustering task as indexing the search results. Here, an index means a structured label list that can make it easier for the user to comprehend the labels and search results. To realize this goal, we make three proposals. The first is to use Named Entity Extraction for term extraction. The second is to create a new label-selecting criterion based on importance in the search result and the relation between terms and search queries. The third is a label categorization using category information of labels, which is generated by named entity extraction. We implement a prototype system based on these proposals and find that it offers a much higher performance than existing methods; we focus on news articles in this article, but the system is not topic specific.