Adapting coreference resolution for narrative processing

Quynh Ngoc Thi Do, Steven Bethard, Marie Francine Moens

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Domain adaptation is a challenge for supervised NLP systems because of expensive and time-consuming manual annotated resources. We present a novel method to adapt a supervised coreference resolution system trained on newswire to short narrative stories without retraining the system. The idea is to perform inference via an Integer Linear Programming (ILP) formulation with the features of narratives adopted as soft constraints. When testing on the UMIREC1 and N22 corpora with the-stateof-the-art Berkeley coreference resolution system trained on OntoNotes3, our inference substantially outperforms the original inference on the CoNLL 2011 metric.

Original languageEnglish (US)
Title of host publicationConference Proceedings - EMNLP 2015
Subtitle of host publicationConference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics (ACL)
Pages2262-2267
Number of pages6
ISBN (Electronic)9781941643327
DOIs
StatePublished - 2015
Externally publishedYes
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal
Duration: Sep 17 2015Sep 21 2015

Publication series

NameConference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

Conference

ConferenceConference on Empirical Methods in Natural Language Processing, EMNLP 2015
Country/TerritoryPortugal
CityLisbon
Period9/17/159/21/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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