Discriminative Learning for Joint Template Filling

Einat Minkov, Luke Zettlemoyer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper presents a joint model for template filling, where the goal is to automatically specify the fields of target relations such as seminar announcements or corporate acquisition events. The approach models mention detection, unification and field extraction in a flexible, feature-rich model that allows for joint modeling of interdependencies at all levels and across fields. Such an approach can, for example, learn likely event durations and the fact that start times should come before end times. While the joint inference space is large, we demonstrate effective learning with a Perceptron-style approach that uses simple, greedy beam decoding. Empirical results in two benchmark domains demonstrate consistently strong performance on both mention detection and template filling tasks.
Original languageEnglish
Title of host publicationProceedings of the 50th Annual Meeting of the Association for Computational Linguistics
Place of PublicationUSA
PublisherAssociation for Computational Linguistics
StatePublished - 2012

Publication series

NameACL '12
PublisherAssociation for Computational Linguistics


Dive into the research topics of 'Discriminative Learning for Joint Template Filling'. Together they form a unique fingerprint.

Cite this