Latent structured prediction theory pro- poses powerful methods such as Latent Structural SVM (LSSVM), which can po- tentially be very appealing for coreference resolution (CR). In contrast, only small work is available, mainly targeting the la- tent structured perceptron (LSP). In this paper, we carried out a practical study comparing for the first time online learn- ing with LSSVM. We analyze the intrica- cies that may have made initial attempts to use LSSVM fail, i.e., huge training time and the much lower accuracy produced by the Kruskal’s spanning tree algorithm. In this respect, we also propose a new effec- tive feature selection approach for improv- ing system efficiency. The results show that LSP, if correctly parameterized, pro- duces the same performance as LSSVM, being at the same time much more effi- cient.

A Practical Perspective on Latent Structured Prediction for Coreference Resolution / Haponchyk, Iryna; Moschitti, Alessandro. - ELETTRONICO. - (2017), pp. 143-149. ((Intervento presentato al convegno ACL 2017 tenutosi a Valencia, Spain nel April 3-7, 2017.

A Practical Perspective on Latent Structured Prediction for Coreference Resolution

Iryna Haponchyk;Alessandro Moschitti
2017

Abstract

Latent structured prediction theory pro- poses powerful methods such as Latent Structural SVM (LSSVM), which can po- tentially be very appealing for coreference resolution (CR). In contrast, only small work is available, mainly targeting the la- tent structured perceptron (LSP). In this paper, we carried out a practical study comparing for the first time online learn- ing with LSSVM. We analyze the intrica- cies that may have made initial attempts to use LSSVM fail, i.e., huge training time and the much lower accuracy produced by the Kruskal’s spanning tree algorithm. In this respect, we also propose a new effec- tive feature selection approach for improv- ing system efficiency. The results show that LSP, if correctly parameterized, pro- duces the same performance as LSSVM, being at the same time much more effi- cient.
Proceedings of the 15th Conference of the European Chapter of theAssociation for Computational Linguistics, EACL 2017, Valencia,Spain, April 3-7, 2017, Volume 2: Short Papers
Iryna Haponchyk; Alessandro Moschitti
Valencia, Spain
Association for Computational Linguistics (ACL)
978-1-945626-35-7
Haponchyk, Iryna; Moschitti, Alessandro
A Practical Perspective on Latent Structured Prediction for Coreference Resolution / Haponchyk, Iryna; Moschitti, Alessandro. - ELETTRONICO. - (2017), pp. 143-149. ((Intervento presentato al convegno ACL 2017 tenutosi a Valencia, Spain nel April 3-7, 2017.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/195364
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