@article{
 title = {Extraction of named entities from tables in gene mutation literature},
 type = {article},
 year = {2009},
 identifiers = {[object Object]},
 pages = {46-54},
 websites = {http://portal.acm.org/citation.cfm?doid=1572364.1572371},
 publisher = {Association for Computational Linguistics},
 id = {c9bc3cf5-6827-31f3-81f5-0e306b48a87d},
 created = {2011-12-29T19:53:53.000Z},
 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 entities},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Wong2009},
 private_publication = {false},
 abstract = {We investigate the challenge of extracting information about genetic mutations from tables, an important source of information in scientific papers. We use various machine learning algorithms and feature sets, and evaluate performance in extracting fields associated with an existing hand-created database of mutations. We then show how classifying tabular information can be leveraged for the task of named entity detection for mutations.},
 bibtype = {article},
 author = {Wong, Wern and Martinez, David and Cavedon, Lawrence},
 journal = {Proceedings of the Workshop on BioNLP BioNLP 09},
 number = {June}
}

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