@inProceedings{
 title = {Exploiting role-identifying nouns and expressions for information extraction},
 type = {inProceedings},
 year = {2007},
 keywords = {event roles,information extraction,learning},
 pages = {165–172},
 websites = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.3802&rep=rep1&type=pdf},
 publisher = {Citeseer},
 id = {4d04d44a-5776-3aad-a441-38e266b65818},
 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},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Phillips2007},
 private_publication = {false},
 abstract = {We present a new approach for extraction pat- tern learning that exploits role-identifying nouns, which are nouns whose semantics reveal the role that they play in an event (e.g., an “assassin” is a perpetrator). Given a few seed nouns, a boot- strapping algorithm automatically learns role- identifying nouns, which are then used to learn extraction patterns. We also introduce a method to learn role-identifying expressions, which con- sist of a role-identifying verb linked to an event (e.g., “<subject> participated in the murder”). We present experimental results on the MUC-4 terrorism corpus and a disease outbreaks corpus.},
 bibtype = {inProceedings},
 author = {Phillips, William and Riloff, Ellen},
 booktitle = {Proceedings of International Conference on Recent Advances in Natural Language Processing}
}

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