Event detection and co-reference with minimal supervision

An important aspect of natural language understanding involves recognizing and categorizing events and the relations among them. However, these tasks are quite subtle and annotating training data for machine learning based approaches is an expensive task, resulting in supervised systems that attempt to learn complex models from small amounts of data, which they over-fit. This paper addresses this challenge by developing an event detection and co-reference system with minimal supervision, in the form of a few event examples. We view these tasks as semantic similarity problems between event mentions or event mentions and an ontology of types, thus facilitating the use of large amounts of out of domain text data. Notably, our semantic relatedness function exploits the structure of the text by making use of a semantic-role-labeling based representation of an event. We show that our approach to event detection is competitive with the top supervised methods. More significantly, we outperform state-of-the-art supervised methods for event coreference on benchmark data sets, and support significantly better transfer across domains.