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Semi-supervised SRL system with Bayesian inference

Alejandra Lorenzo 1 Christophe Cerisara 1
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : We propose a new approach to perform semi-supervised training of Semantic Role Labeling models with very few amount of initial labeled data. The proposed approach combines in a novel way supervised and unsupervised training, by forcing the supervised classifier to over-generate potential semantic candidates, and then letting unsupervised inference choose the best ones. Hence, the supervised classifier can be trained on a very small corpus and with coarse-grain features, because its precision does not need to be high: its role is mainly to constrain Bayesian inference to explore only a limited part of the full search space. This approach is evaluated on French and English. In both cases, it achieves very good performance and outperforms a strong supervised baseline when only a small number of annotated sentences is available and even without using any previously trained syntactic parser.
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Contributor : Christophe Cerisara <>
Submitted on : Thursday, June 26, 2014 - 2:24:02 PM
Last modification on : Tuesday, September 24, 2019 - 4:00:09 PM
Long-term archiving on: : Friday, September 26, 2014 - 12:11:10 PM


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  • HAL Id : hal-01015414, version 1



Alejandra Lorenzo, Christophe Cerisara. Semi-supervised SRL system with Bayesian inference. 15th International Conference on Intelligent Text Processing and Computational Linguistics, Apr 2014, Kathmandu, Nepal. pp.433. ⟨hal-01015414⟩



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