Paper: Semi-supervised Semantic Role Labeling Using the Latent Words Language Model

ACL ID D09-1003
Title Semi-supervised Semantic Role Labeling Using the Latent Words Language Model
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

Semantic Role Labeling (SRL) has proved to be a valuable tool for performing auto- matic analysis of natural language texts. Currently however, most systems rely on a large training set, which is manually an- notated, an effort that needs to be repeated whenever different languages or a differ- ent set of semantic roles is used in a cer- tain application. A possible solution for this problem is semi-supervised learning, where a small set of training examples is automatically expanded using unlabeled texts. We present the Latent Words Lan- guage Model, which is a language model that learns word similarities from unla- beled texts. We use these similarities for different semi-supervised SRL methods as additional features or to automatically ex- pand a small training set. We evaluate the meth...