CUED Publications database

Enhanced discriminative models with tree kernels and unsupervised training for entity detection

Rojas-Barahona, LM and Cerisara, C (2015) Enhanced discriminative models with tree kernels and unsupervised training for entity detection. In: UNSPECIFIED pp. 38-45..

Full text not available from this repository.

Abstract

© 2015 IEEE. This work explores two approaches to improve the discriminative models that are commonly used nowadays for entity detection: tree-kernels and unsupervised training. Feature-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. The first proposed approach consists in leveraging the fast and efficient linear models with unsupervised training, thanks to a recently proposed approximation of the classifier risk, an appealing method that provably converges towards the minimum risk without any labeled corpus. In the second proposed approach, tree kernels are used with support vector machines to exploit dependency structures for entity detection, which relieve designers from the burden of carefully design rich syntactic features manually. We study both approaches on the same task and corpus and show that they offer interesting alternatives to supervised learning for 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: