TY - JOUR
T1 - Finding related entities by retrieving relations
T2 - 18th Text REtrieval Conference, TREC 2009
AU - Vinod Vydiswaran, V. G.
AU - Ganesan, Kavita
AU - Lv, Yuanhua
AU - He, Jing
AU - Zhai, Chengxiang
N1 - Funding Information:
This work is supported by Geneva International Academic Network (GIAN), research project "Lin- guistic Analysis and Collocation Extraction", approved in 2001. Thanks to Olivier Pasteur for the invaluable help in this research.
PY - 2009
Y1 - 2009
N2 - Our goal in participating in the TREC 2009 Entity Track was to study whether relation extraction techniques can help in improving accuracy of the entity finding task. Finding related entities is informational in nature and we wanted to explore if inducing structure on the queries helps satisfy this information need. The research outlook we took was to study techniques that retrieve relations between two entities from a large corpus, and from those, find the most relevant entities that participate in the given relation with another given entity. Instead of aiming at retrieving pages about specific entities, we tried to address the problem of directly finding the entities from the text. Our experimental results show that we were able to find many related entities using relation-based extraction, and ranking entities based on further evidence from the text helps to a certain extent.
AB - Our goal in participating in the TREC 2009 Entity Track was to study whether relation extraction techniques can help in improving accuracy of the entity finding task. Finding related entities is informational in nature and we wanted to explore if inducing structure on the queries helps satisfy this information need. The research outlook we took was to study techniques that retrieve relations between two entities from a large corpus, and from those, find the most relevant entities that participate in the given relation with another given entity. Instead of aiming at retrieving pages about specific entities, we tried to address the problem of directly finding the entities from the text. Our experimental results show that we were able to find many related entities using relation-based extraction, and ranking entities based on further evidence from the text helps to a certain extent.
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M3 - Conference article
AN - SCOPUS:84873445539
SN - 1048-776X
JO - NIST Special Publication
JF - NIST Special Publication
Y2 - 17 November 2009 through 20 November 2009
ER -