A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Exploiting rich syntactic information for relation extraction from biomedical articles
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Exploiting rich syntactic information for relation extraction from biomedical articles
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
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In this paper we propose a statistical parsing technique that simultaneously identifies biomedical named-entities (NEs) and extracts subcellular localization relations for bacterial proteins from the text in MEDLINE articles. We build a parser that derives both syntactic and domain-dependent semantic information and achieves an F-score of 48.4% for the relation extraction task. We then propose a semi-supervised approach that incorporates noisy automatically labeled data to improve the F-score of our parser to 83.2%. Our key contributions are: learning from noisy data, and building an annotated corpus that can benefit relation extraction research.