Paper: Unsupervised Learning of Narrative Schemas and their Participants

ACL ID P09-1068
Title Unsupervised Learning of Narrative Schemas and their Participants
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

We describe an unsupervised system for learn- ing narrative schemas, coherent sequences or sets of events (arrested(POLICE,SUSPECT), convicted( JUDGE, SUSPECT)) whose arguments are filled with participant semantic roles defined over words (JUDGE = {judge, jury, court}, POLICE = {police, agent, authorities}). Unlike most previous work in event structure or semantic role learning, our sys- tem does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles. Our unsupervised learning algorithm uses corefer- ring arguments in chains of verbs to learn both rich narrative event structure and argument roles. By jointly addressing both tasks, we improve on pre- vious results in narrative/frame learning and induce rich frame-specific semantic roles.