Trained named entity recognition using distributional clusters. Freitag, D. Proceedings of EMNLP, 2004.
Trained named entity recognition using distributional clusters [pdf]Website  abstract   bibtex   
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.

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