Effective Information Extraction with Semantic Affinity Patterns and Relevant Regions

Effective Information Extraction with Semantic Affinity Patterns and Relevant Regions. Patwardhan, S. & Riloff, E. Computational Linguistics, 2007.

Website abstract bibtex

We present an information extraction system that decouples the tasks of ﬁnding relevant regions of text and applying extraction pat- terns. We create a self-trained relevant sen- tence classiﬁer to identify relevant regions, and use a semantic afﬁnity measure to au- tomatically learn domain-relevant extraction patterns. We then distinguish primary pat- terns from secondary patterns and apply the patterns selectively in the relevant regions. The resulting IE system achieves good per- formance on the MUC-4 terrorism corpus and ProMed disease outbreak stories. This approach requires only a few seed extraction patterns and a collection of relevant and ir- relevant documents for training.

@article{
 title = {Effective Information Extraction with Semantic Affinity Patterns and Relevant Regions},
 type = {article},
 year = {2007},
 pages = {717-727},
 websites = {http://www.aclweb.org/anthology/D/D07/D07-1075},
 id = {c5f637e7-a12c-3161-ad7c-5ed99786a7ba},
 created = {2012-02-28T00:51:15.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
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 last_modified = {2017-03-14T14:36:19.698Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Patwardhan2007},
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 abstract = {We present an information extraction system that decouples the tasks of ﬁnding relevant regions of text and applying extraction pat- terns. We create a self-trained relevant sen- tence classiﬁer to identify relevant regions, and use a semantic afﬁnity measure to au- tomatically learn domain-relevant extraction patterns. We then distinguish primary pat- terns from secondary patterns and apply the patterns selectively in the relevant regions. The resulting IE system achieves good per- formance on the MUC-4 terrorism corpus and ProMed disease outbreak stories. This approach requires only a few seed extraction patterns and a collection of relevant and ir- relevant documents for training.},
 bibtype = {article},
 author = {Patwardhan, Siddharth and Riloff, Ellen},
 journal = {Computational Linguistics},
 number = {June}
}

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