An ensemble model that combines syntactic and semantic clustering for discriminative dependency parsing

Authors:
Gholamreza Haffari;Marzieh Razavi;Anoop Sarkar
Affiliations:
Monash University, Melbourne, Australia;Simon Fraser University, Vancouver, Canada;Simon Fraser University, Vancouver, Canada
Venue:
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Year:
2011

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Abstract

We combine multiple word representations based on semantic clusters extracted from the (Brown et al., 1992) algorithm and syntactic clusters obtained from the Berkeley parser (Petrov et al., 2006) in order to improve discriminative dependency parsing in the MST-Parser framework (McDonald et al., 2005). We also provide an ensemble method for combining diverse cluster-based models. The two contributions together significantly improves unlabeled dependency accuracy from 90.82% to 92.13%.