Entity Extraction via Ensemble Semantics

Combining information extraction systems yields significantly higher quality resources 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 patternbased systems with a large set of features from a webcrawl, query logs, and Wikipedia. Experimental results on a webscale extraction of actors, athletes and musicians show significantly higher mean average precision scores (29% gain) compared with the current state of the art.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Marco Pennacchiotti, Patrick Pantel
• Keywords: EMNLP 2009, Information Extraction Systems, Mean Average Precision, Natural Language Processing, Webscale Extraction