Event Schema Induction Based on Relational Co-occurrence over Multiple Documents

Abstract

Event schema which comprises a set of related events and participants is of great importance with the development of information extraction (IE) and inducing event schema is prerequisite for IE and natural language generation. Event schema and slots are usually designed manually for traditional IE tasks. Methods for inducing event schemas automatically have been proposed recently. One of the fundamental assumptions in event schema induction is that related events tend to appear together to describe a scenario in natural-language discourse, meanwhile previous work only focused on co-occurrence in one document. We find that semantically typed relational tuples co-occurrence over multiple documents is helpful to construct event schema. We exploit the relational tuples co-occurrence over multiple documents by locating the key tuple and counting relational tuples, and build a co-occurrence graph which takes account of co-occurrence information over multiple documents. Experiments show that co-occurrence information over multiple documents can help to combine similar elements of event schema as well as to alleviate incoherence problems.