Paper: Open-Domain Semantic Role Labeling by Modeling Word Spans

ACL ID P10-1099
Title Open-Domain Semantic Role Labeling by Modeling Word Spans
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010

Most supervised language processing sys- tems show a significant drop-off in per- formance when they are tested on text that comes from a domain significantly different from the domain of the training data. Semantic role labeling techniques are typically trained on newswire text, and in tests their performance on fiction is as much as 19% worse than their perfor- mance on newswire text. We investigate techniques for building open-domain se- mantic role labeling systems that approach the ideal of a train-once, use-anywhere system. We leverage recently-developed techniques for learning representations of text using latent-variable language mod- els, and extend these techniques to ones that provide the kinds of features that are useful for semantic role labeling. In exper- iments, our novel s...