@article{
 title = {Event extraction from biomedical papers using a full parser.},
 type = {article},
 year = {2001},
 identifiers = {[object Object]},
 keywords = {automatic data processing,databases,factual,natural language processing},
 pages = {408-419},
 volume = {419},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/11262959},
 institution = {Department of Information Science, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.},
 id = {7d15d1a2-25f4-3c4e-b0e9-1a312e9dca42},
 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 = {event extraction},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Yakushiji2001},
 private_publication = {false},
 abstract = {We have designed and implemented an information extraction system using a full parser to investigate the plausibility of full analysis of text using general-purpose parser and grammar applied to biomedical domain. We partially solved the problems of full parsing of inefficiency, ambiguity, and low coverage by introducing the preprocessors, and proposed the use of modules that handles partial results of parsing for further improvement. Our approach makes it possible to modularize the system, so that the IE system as a whole becomes easy to be tuned to specific domains, and easy to be maintained and improved by incorporating various techniques of disambiguation, speed up, etc. In preliminary experiment, from 133 argument structures that should be extracted from 97 sentences, we obtained 23% uniquely and 24% with ambiguity. And 20% are extractable from not complete but partial results of full parsing.},
 bibtype = {article},
 author = {Yakushiji, A and Tateisi, Y and Miyao, Y and Tsujii, J},
 journal = {Pacific Symposium On Biocomputing},
 number = {419}
}

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