Unsupervised Discovery of Negative Categories in Lexicon Bootstrapping

Multi-category bootstrapping algorithms were developed to reduce semantic drift. By extracting multiple semantic lexicons simultaneously, a category's search space may be restricted. 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 towards a supervised framework. We present NEG-FINDER, the first approach for discovering negative categories automatically. NEG-FINDER exploits unsupervised term clustering to generate multiple negative categories during bootstrapping. Our algorithm effectively removes the necessity of manual intervention and formulation of negative categories, with performance closely approaching that obtained using negative categories defined by a domain expert.

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Tara McIntosh
• Keywords: EMNLP 2010, Multi-category Bootstrapping Algorithms, Multiple Negative Categories, Natural Language Processing, Negative Categories