Paper: Automatic Event Extraction with Structured Preference Modeling

ACL ID P12-1088
Title Automatic Event Extraction with Structured Preference Modeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012

This paper presents a novel sequence label- ing model based on the latent-variable semi- Markov conditional random fields for jointly extracting argument roles of events from texts. The model takes in coarse mention and type information and predicts argument roles for a given event template. This paper addresses the event extraction problem in a primarily unsupervised setting, where no labeled training instances are avail- able. Our key contribution is a novel learning framework called structured preference mod- eling (PM), that allows arbitrary preference to be assigned to certain structures during the learning procedure. We establish and discuss connections between this framework and other existing works. We show empirically that the structured preferences are crucial to the suc- cess of...