Paper: Unsupervised Discovery of Negative Categories in Lexicon Bootstrapping

ACL ID D10-1035
Title Unsupervised Discovery of Negative Categories in Lexicon Bootstrapping
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Multi-category bootstrapping algorithms were developed to reduce semantic drift. By ex- tracting multiple semantic lexicons simultane- ously, a category’s search space may be re- stricted. The best results have been achieved through reliance on manually crafted negative categories. Unfortunately, identifying these categories is non-trivial, and their use shifts the unsupervised bootstrapping paradigm to- wards a supervised framework. We present NEG-FINDER, the first approach for discovering negative categories automat- ically. NEG-FINDER exploits unsupervised term clustering to generate multiple nega- tive categories during bootstrapping. Our al- gorithm effectively removes the necessity of manual intervention and formulation of nega- tive categories, with performance closely ap- proachi...