Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2011

Abstract

In this paper we present a novel approach to entity linking based on a statistical language model-based information retrieval with query expansion. We use both local contexts and global world knowledge to expand query language models. We place a strong emphasis on named entities in the local contexts and explore a positional language model to weigh them differently based on their distances to the query. Our experiments on the TAC-KBP 2010 data show that incorporating such contextual information indeed aids in disambiguating the named entities and consistently improves the entity linking performance. Compared with the official results from KBP 2010 participants, our system shows competitive performance.

Keywords

Contextual information, Knowledge base, Language model, Named entities, Query expansion, Query language model, World knowledge

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

EMNLP '11: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK, July 27-31

First Page

804

Last Page

813

ISBN

9781937284114

Publisher

Association for Computational Linguistics

City or Country

Stroudsburg, PA

Copyright Owner and License

Authors

Additional URL

http://aclweb.org/anthology/D/D11/D11-1074.pdf

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