Inducing Domain-Specific Semantic Class Taggers from (Almost) Nothing Conference Paper uri icon


  • This research explores the idea of inducing domain-specific semantic class taggers using only a domain-specific text collection and seed words. The learning process begins by inducing a classifier that only has access to contextual features, forcing it to generalize beyond the seeds. The contextual classifier then labels new instances, to expand and diversify the training set. Next, a cross-category bootstrapping process simultaneously trains a suite of classifiers for multiple semantic classes. The positive instances for one class are used as negative instances for the others in an iterative bootstrapping cycle. We also explore a one-semantic-class-per-discourse heuristic, and use the classifiers to dynamically create semantic features. We evaluate our approach by inducing six semantic taggers from a collection of veterinary medicine message board posts.

published proceedings

  • Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

author list (cited authors)

  • Huang, R., & Riloff, E.

complete list of authors

  • Huang, Ruihong||Riloff, Ellen

publication date

  • January 2010