Generating Coherent Event Schemas at Scale
Niranjan Balasubramanian, Stephen Soderland, Mausam, Oren Etzioni · 2013
Chambers and Jurafsky (2009) demonstrated that event schemas can be automatically induced from text corpora.However, our analysis of their schemas identifies several weaknesses, e.g., some schemas lack a common topic and distinct roles are incorrectly mixed into a single actor.It is due in part to their pair-wise representation that treats subjectverb independently from verb-object.This often leads to subject-verb-object triples that are not meaningful in the real-world.We present a novel approach to inducing open-domain event schemas that overcomes these limitations.Our approach uses cooccurrence statistics of semantically typed relational triples, which we call Rel-grams (relational n-grams).In a human evaluation, our schemas outperform Chambers's schemas by wide margins on several evaluation criteria.Both Rel-grams and event schemas are freely available to the research community.