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NLP Reading group - Template-Based Information Extraction without the Templates

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“Template-Based Information Extraction without the Templates”. By Nathanael Chambers and Dan Jurafsky

Abstract:

“Standard algorithms for template-based information extraction (IE) require predefined template schemas, and often labeled data, to learn to extract their slot fillers (e.g., an embassy is the Target of a Bombing template). This paper describes an approach to template-based IE that removes this requirement and performs extraction without knowing the template structure in advance. Our algorithm instead learns the template structure automatically from raw text, inducing template schemas as sets of linked events (e.g., bombings include detonate, set off, and destroy events) associated with semantic roles. We also solve the standard IE task, using the induced syntactic patterns to extract role fillers from specific documents. We evaluate on the MUC -4 terrorism dataset and show that we induce template structure very similar to hand-created gold structure, and we extract role fillers with an F1 score of .40, approaching the performance of algorithms that require full knowledge of the templates.”

It would be presented at ACL2011 and is available at: http://www.stanford.edu/~jurafsky/acl2011-chambers-templates.pdf

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

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