Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training

Quynh Ngoc Thi Do, Bethard Steven, Marie Francine Moens

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

2 Scopus citations

Abstract

We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited. In this work, co-training is used together with word embeddings to improve the performance of a system trained on CoNLL 2009 training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results.

Original languageEnglish (US)
Title of host publicationCOLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016
Subtitle of host publicationTechnical Papers
PublisherAssociation for Computational Linguistics, ACL Anthology
Pages1275-1284
Number of pages10
ISBN (Print)9784879747020
StatePublished - 2016
Event26th International Conference on Computational Linguistics, COLING 2016 - Osaka, Japan
Duration: Dec 11 2016Dec 16 2016

Publication series

NameCOLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers

Other

Other26th International Conference on Computational Linguistics, COLING 2016
Country/TerritoryJapan
CityOsaka
Period12/11/1612/16/16

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Language and Linguistics
  • Linguistics and Language

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