@inProceedings{
 title = {Entity extraction without language-specific resources},
 type = {inProceedings},
 year = {2002},
 identifiers = {[object Object]},
 pages = {1-4},
 websites = {http://portal.acm.org/citation.cfm?id=1118873},
 publisher = {Association for Computational Linguistics},
 id = {c5ce4b5e-cb7b-3970-bac3-277d81936c0b},
 created = {2012-02-28T00:52:49.000Z},
 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 = {McNamee2002},
 private_publication = {false},
 abstract = {We describe a named-entity tagging system that requires minimal linguistic knowledge and thus may be applied to new target languages without significant adaptation. To maintain a language- neutral posture, the system is linguistically nave, and in fact, reduces the tagging problem to supervised machine learning. A large number of binary features are extracted from labeled data to train classifiers and computationally expensive features are eschewed. We have initially focused our attention on linear support vectors machines (SVMs); SVMs are known to work well when a large number of features is used as long as the individual vectors are sparse. We call our system SNOOD (Hopkins APL Inductive Retargetable Named Entity Tagger).},
 bibtype = {inProceedings},
 author = {McNamee, Paul and Mayfield, James},
 booktitle = {Proceedings of the 6th Conference on Natural Language Learning CoNLL02}
}

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