CUED Publications database

Weakly supervised discriminative training of linear models for natural language processing

Rojas-Barahona, LM and Cerisara, C (2015) Weakly supervised discriminative training of linear models for natural language processing. In: UNSPECIFIED pp. 242-254..

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Abstract

© Springer International Publishing Switzerland 2015. This work explores weakly supervised training of discriminative linear classifiers. Such features-rich classifiers have been widely adopted by the Natural Language processing (NLP) community because of their powerful modeling capacity and their support for correlated features, which allow separating the expert task of designing features from the core learning method. However, unsupervised training of discriminative models is more challenging than with generative models. We adapt a recently proposed approximation of the classifier risk and derive a closed-form solution that greatly speeds-up its convergence time. This method is appealing because it provably converges towards the minimum risk without any labeled corpus, thanks to only two reasonable assumptions about the rank of class marginal and Gaussianity of class-conditional linear scores. We also show that the method is a viable, interesting alternative to achieve weakly supervised training of linear classifiers in two NLP tasks: predicate and entity recognition.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:29
Last Modified: 16 Nov 2017 02:19
DOI: