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Unsupervised Learning of Narrative Schemas and their Participants

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If you have a question about this talk, please contact Diarmuid Ó Séaghdha.

At this session of the NLIP Reading Group we’ll be discussing the following paper:

Nathaniel Chambers and Dan Jurafsky. 2009. Unsupervised Learning of Narrative Schemas and their Participants. In Proceedings of ACL -IJCNLP-09.

Abstract: We describe an unsupervised system for learning 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 system does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles. Our unsupervised learning algorithm uses coreferring arguments in chains of verbs to learn both rich narrative event structure and argument roles. By jointly addressing both tasks, we improve on previous results in narrative/frame learning and induce rich frame-specific semantic roles.

This talk is part of the Natural Language Processing Reading Group series.

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