CiteSeerX — Trained Named Entity Recognition Using Distributional Clusters

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Trained Named Entity Recognition Using Distributional Clusters

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@MISC{_trainednamed,
    author = {},
    title = {Trained Named Entity Recognition Using Distributional Clusters},
    year = {}
}

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Abstract

This work applies boosted wrapper induction (BWI), a machine learning algorithm for information extraction from semi-structured documents, to the problem of named entity recognition. The default feature set of BWI is augmented with features based on distributional term clusters induced from a large unlabeled text corpus. Using no traditional linguistic resources, such as syntactic tags or specialpurpose gazetteers, this approach yields results near the state of the art in the MUC 6 named entity domain. Supervised learning using features derived through unsupervised corpus analysis may be regarded as an alternative to bootstrapping methods. 1

Keyphrases

entity domain large unlabeled text corpus default feature machine learning algorithm distributional term cluster specialpurpose gazetteer information extraction semi-structured document entity recognition syntactic tag traditional linguistic resource unsupervised corpus analysis