An Approach of Filtering Wrong-Type Entities for Entity Ranking

Junsan ZHANG  Youli QU  Shu GONG  Shengfeng TIAN  Haoliang SUN  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E96-D    No.1    pp.163-167
Publication Date: 2013/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.163
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Natural Language Processing
Keyword: 
related entity finding,  entity ranking,  type filtering,  wikipedia,  

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Summary: 
Entity is an important information carrier in Web pages. Users would like to directly get a list of relevant entities instead of a list of documents when they submit a query to the search engine. So the research of related entity finding (REF) is a meaningful work. In this paper we investigate the most important task of REF: Entity Ranking. The wrong-type entities which don't belong to the target-entity type will pollute the ranking result. We propose a novel method to filter wrong-type entities. We focus on the acquisition of seed entities and automatically extracting the common Wikipedia categories of target-entity type. Also we demonstrate how to filter wrong-type entities using the proposed model. The experimental results show our method can filter wrong-type entities effectively and improve the results of entity ranking.