Paper: Event Schema Induction with a Probabilistic Entity-Driven Model

ACL ID D13-1185
Title Event Schema Induction with a Probabilistic Entity-Driven Model
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Event schema induction is the task of learning high-level representations of complex events (e.g., a bombing) and their entity roles (e.g., perpetrator and victim) from unlabeled text. Event schemas have important connections to early NLP research on frames and scripts, as well as modern applications like template extraction. Recent research suggests event schemas can be learned from raw text. In- spired by a pipelined learner based on named entity coreference, this paper presents the first generative model for schema induction that in- tegrates coreference chains into learning. Our generative model is conceptually simpler than the pipelined approach and requires far less training data. It also provides an interesting contrast with a recent HMM-based model. We evaluate on a common dataset ...