High-order low-rank tensors for semantic role labeling
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Barzilay_High-order low-rank tensors.pdf
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Author(s)
Lei, Tao
Zhang, Yuan
Marquez, Lluis
Moschitti, Alessandro
Barzilay, Regina
Date Issued
June 2015
Journal
NAACL HLT 2015, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Publisher
Association for Computational Linguistics
Citation
Lei, Tao et al. "High-Order Low-Rank Tensors for Semantic Role Labeling." NAACL HLT 2015, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, USA, May 31 - June 5, 2015. Association for Computational Linguistics, 2015.
Version
Author's final manuscript
Abstract
This paper introduces a tensor-based approach to semantic role labeling (SRL). The motivation behind the approach is to automatically
induce a compact feature representation for words and their relations, tailoring them to the task. In this sense, our dimensionality reduction method provides a clear alternative to the traditional feature engineering approach
used in SRL. To capture meaningful interactions between the argument, predicate, their syntactic path and the corresponding role label,
we compress each feature representation first to a lower dimensional space prior to assessing their interactions. This corresponds to using an overall cross-product feature representation and maintaining associated parameters
as a four-way low-rank tensor. The tensor parameters are optimized for the SRL performance using standard online algorithms. Our tensor-based approach rivals the best performing system on the CoNLL-2009 shared task.
In addition, we demonstrate that adding the representation tensor to a competitive tensorfree model yields 2% absolute increase in Fscore.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
http://dblp.dagstuhl.de/db/conf/naacl/naacl2015.html
