@article{
 title = {Trained named entity recognition using distributional clusters},
 type = {article},
 year = {2004},
 volume = {4},
 websites = {http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Freitag.pdf},
 id = {d65ce704-28d3-36bc-a753-57e773bef807},
 created = {2012-02-28T00:51:15.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {named entity recognition},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Freitag2004},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Freitag, Dayne},
 journal = {Proceedings of EMNLP}
}

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