@inProceedings{
 title = {Automated Subcategorization of Named Entities},
 type = {inProceedings},
 year = {2001},
 pages = {25–30},
 volume = {39},
 websites = {http://acl.ldc.upenn.edu/acl2001/STUDENT/fleischman.pdf},
 publisher = {UNKNOWN},
 id = {3392bffa-bafa-39e8-9120-0004953ea289},
 created = {2012-04-01T16:32:49.000Z},
 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 = {Fleischman2001},
 private_publication = {false},
 abstract = {There has been much interest in the recent past concerning the possibilities for automated categorization of named entities. The research presented here describes a method for the subcategorization of location names. Subcategorization of locations is not a trivial task even for human subjects, who perform at accuracy levels of less than 58%. After experimenting with both Bayesian classifiers and decision tree learning algorithms, we have designed a system that achieves accuracy levels greater than 80%.},
 bibtype = {inProceedings},
 author = {Fleischman, Michael and Rey, Marina},
 booktitle = {Methods}
}

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