AIC Seminar Series
Unsupervised Discovery of Narratives from Text
|Nate Chambers||Stanford University|
Notice: hosted by Eric Yeh
Date: Thursday, May 22nd 2008 at 4:00pm
Location: EJ228 (SRI E building) (Directions)
Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. I'll describe a proposed unsupervised induction of similar schemata called Narrative Event Chains from raw newswire text. A narrative chain is a partially ordered set of events related by a common protagonist. I'll describe a three step process to learning narrative chains. The first uses unsupervised distributional methods to learn narrative relations between events sharing coreferring entity arguments. The second applies a temporal classifier to partially order the connected events. Finally, the third prunes and clusters self-contained chains from the space of events. Two evaluations are introduced: the narrative cloze to evaluate event relatedness, and an order coherence task to evaluate narrative order.
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