Paper: Entity Extraction via Ensemble Semantics

ACL ID D09-1025
Title Entity Extraction via Ensemble Semantics
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

Combining information extraction sys- tems yields significantly higher quality re- sources than each system in isolation. In this paper, we generalize such a mixing of sources and features in a framework called Ensemble Semantics. We show very large gains in entity extraction by combining state-of-the-art distributional and pattern- based systems with a large set of fea- tures from a webcrawl, query logs, and Wikipedia. Experimental results on a web- scale extraction of actors, athletes and mu- sicians show significantly higher mean av- erage precision scores (29% gain) com- pared with the current state of the art.