IEICE TRANSACTIONS on Information
An Approach of Filtering Wrong-Type Entities for Entity Ranking
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.1 pp.163-167
- Publication Date
- 2013/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.163
- Type of Manuscript
- LETTER
- Category
- Natural Language Processing
Authors
Keyword
Latest Issue
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Junsan ZHANG, Youli QU, Shu GONG, Shengfeng TIAN, Haoliang SUN, "An Approach of Filtering Wrong-Type Entities for Entity Ranking" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 1, pp. 163-167, January 2013, doi: 10.1587/transinf.E96.D.163.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.163/_p
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@ARTICLE{e96-d_1_163,
author={Junsan ZHANG, Youli QU, Shu GONG, Shengfeng TIAN, Haoliang SUN, },
journal={IEICE TRANSACTIONS on Information},
title={An Approach of Filtering Wrong-Type Entities for Entity Ranking},
year={2013},
volume={E96-D},
number={1},
pages={163-167},
abstract={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.},
keywords={},
doi={10.1587/transinf.E96.D.163},
ISSN={1745-1361},
month={January},}
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TY - JOUR
TI - An Approach of Filtering Wrong-Type Entities for Entity Ranking
T2 - IEICE TRANSACTIONS on Information
SP - 163
EP - 167
AU - Junsan ZHANG
AU - Youli QU
AU - Shu GONG
AU - Shengfeng TIAN
AU - Haoliang SUN
PY - 2013
DO - 10.1587/transinf.E96.D.163
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2013
AB - 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.
ER -
English
