2010 InducingDomainSpecificSemanticC

• (Huang et al., 2010) ⇒ Ruihong Huang, and Ellen Riloff. (2010). “Inducing Domain-specific Semantic Class Taggers from (almost) Nothing.” In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Inducing+domain-specific+semantic+class+taggers+from+%28almost%29+nothing%22+2010
• http://dl.acm.org/citation.cfm?id=1858681.1858710&preflayout=flat#citedby

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 InducingDomainSpecificSemanticC	Ellen Riloff Ruihong Huang			Inducing Domain-specific Semantic Class Taggers from (almost) Nothing