Paper: Minority Vote: At-Least-N Voting Improves Recall For Extracting Relations

ACL ID P06-2060
Title Minority Vote: At-Least-N Voting Improves Recall For Extracting Relations
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

Several NLP tasks are characterized by asymmetric data where one class label NONE, signifying the absence of any structure (named entity, coreference, re- lation, etc). dominates all other classes. Classifiers built on such data typically have a higher precision and a lower re- call and tend to overproduce the NONE class. We present a novel scheme for vot- ing among a committee of classifiers that can significantly boost the recall in such situations. We demonstrate results show- ing up to a 16% relative improvement in ACE value for the 2004 ACE relation ex- traction task for English, Arabic and Chi- nese.