@inProceedings{
 title = {Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations},
 type = {inProceedings},
 year = {2004},
 identifiers = {[object Object]},
 pages = {22},
 websites = {http://portal.acm.org/citation.cfm?doid=1219044.1219066},
 publisher = {Association for Computational Linguistics},
 id = {9959d486-78e0-3a81-bd01-3e1bc6f7cb5d},
 created = {2010-11-13T05:49:42.000Z},
 file_attached = {true},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {relation extraction},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Kambhatla2004},
 private_publication = {false},
 abstract = {Extracting semantic relationships between entities is challenging because of a paucity of annotated data and the errors induced by entity detection modules. We employ Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text. Our system obtained competitive results in the Automatic Content Extraction (ACE) evaluation. Here we present our general approach and describe our ACE results.},
 bibtype = {inProceedings},
 author = {Kambhatla, Nanda},
 booktitle = {Proceedings of the ACL 2004 on Interactive poster and demonstration sessions}
}

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