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Physics lego figure. Photo.

Anders Björkelund

Postdoc

Physics lego figure. Photo.

Multilingual semantic role labeling

Author

  • Anders Björkelund
  • Love Hafdell
  • Pierre Nugues

Summary, in English

This paper describes our contribution to the semantic role labeling task (SRL-only) of the CoNLL-2009 shared task in the closed challenge(Hajic et al., 2009). Our system consists of a pipeline of independent, local classifiers

that identify the predicate sense, the arguments of the predicates, and the argument labels. Using these local models, we carried out a beam search to generate a pool of candidates. We then reranked the candidates using a joint learning approach that combines the local models and proposition features.

To address the multilingual nature of the data, we implemented a feature selection procedure that systematically explored the feature space, yielding significant gains over a standard set of features. Our system achieved the second best semantic score overall with an average labeled semantic F1 of 80.31. It obtained the best F1 score on the Chinese and German data

and the second best one on English.

Department/s

  • Department of Computer Science
  • Robotics and Semantic Systems

Publishing year

2009

Language

English

Pages

43-48

Publication/Series

Proceedings of The Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

Document type

Conference paper

Topic

  • Computer Science

Status

Published