Generative vs Discriminative approaches to entity Recognition from label deficient data. Goutte, C., Gaussier, E., Cancedda, N., & Dejean, H. Machine Learning, 2004. ![Generative vs Discriminative approaches to entity Recognition from label deficient data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex Annotating biomedical text for Named Entity Recognition (NER) is usually a tedious and expensive process, while unannotated data is freely available in large quantities. It therefore seems relevant to address biomedical NER using Machine Learning techniques that learn from a combination of labelled and unlabelled data. We consider two approaches: one is discriminative, using Support Vector Machines, the other generative, using mixture models. We compare the two on a biomedical NER task with various levels of annotation, and different similarity measures. We also investigate the use of Fisher kernels as a way to leverage the strength of both approaches. Overall the discriminative approach using standard similarity measures seems to out-perform both the generative approach and the Fisher kernels.
@article{
title = {Generative vs Discriminative approaches to entity Recognition from label deficient data},
type = {article},
year = {2004},
keywords = {information retrieval & textual information access,learning,natural language processing,statistics & optimisation},
websites = {http://eprints.pascal-network.org/archive/00000551/},
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created = {2012-01-21T12:35:31.000Z},
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last_modified = {2017-03-14T14:36:19.698Z},
tags = {named entity recognition},
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abstract = {Annotating biomedical text for Named Entity Recognition (NER) is usually a tedious and expensive process, while unannotated data is freely available in large quantities. It therefore seems relevant to address biomedical NER using Machine Learning techniques that learn from a combination of labelled and unlabelled data. We consider two approaches: one is discriminative, using Support Vector Machines, the other generative, using mixture models. We compare the two on a biomedical NER task with various levels of annotation, and different similarity measures. We also investigate the use of Fisher kernels as a way to leverage the strength of both approaches. Overall the discriminative approach using standard similarity measures seems to out-perform both the generative approach and the Fisher kernels.},
bibtype = {article},
author = {Goutte, Cyril and Gaussier, Eric and Cancedda, Nicola and Dejean, Herve},
journal = {Machine Learning}
}
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