Paper: Linking Entities to a Knowledge Base with Query Expansion

ACL ID D11-1074
Title Linking Entities to a Knowledge Base with Query Expansion
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

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