A Practical Perspective on Latent Structured Prediction for Coreference Resolution

Iryna Haponchyk, Alessandro Moschitti


Abstract
Latent structured prediction theory proposes powerful methods such as Latent Structural SVM (LSSVM), which can potentially be very appealing for coreference resolution (CR). In contrast, only small work is available, mainly targeting the latent structured perceptron (LSP). In this paper, we carried out a practical study comparing for the first time online learning with LSSVM. We analyze the intricacies that may have made initial attempts to use LSSVM fail, i.e., a huge training time and much lower accuracy produced by Kruskal’s spanning tree algorithm. In this respect, we also propose a new effective feature selection approach for improving system efficiency. The results show that LSP, if correctly parameterized, produces the same performance as LSSVM, being much more efficient.
Anthology ID:
E17-2023
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
143–149
Language:
URL:
https://aclanthology.org/E17-2023
DOI:
Bibkey:
Cite (ACL):
Iryna Haponchyk and Alessandro Moschitti. 2017. A Practical Perspective on Latent Structured Prediction for Coreference Resolution. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 143–149, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
A Practical Perspective on Latent Structured Prediction for Coreference Resolution (Haponchyk & Moschitti, EACL 2017)
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PDF:
https://aclanthology.org/E17-2023.pdf
Data
CoNLL-2012