@article{
 title = {Information extraction by text classification},
 type = {article},
 year = {2004},
 pages = {1-7},
 websites = {http://citeseer.ist.psu.edu/652372.html;},
 id = {bce0a6f6-42b5-3adf-bb00-d57398ca6db0},
 created = {2011-12-28T07:04:55.000Z},
 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 = {named entities},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Kushmerick2004},
 private_publication = {false},
 abstract = {We investigate the application of classification techniques to the problem of information extraction (IE). In particular we use support vector machines and several different feature-sets to build a set of classifiers for information extraction. We show that this approach is competitive with current state-ofthe -art information extraction algorithms based on specialized learning algorithms. We also introduce a new technique for improving the recall of IE systems called convergent boundary classification. We show that this can give significant improvement in the performance of our IE system and gives a system with both high precision and high recall.},
 bibtype = {article},
 author = {Kushmerick, Nicholas and Johnston, Edward and Mcguinness, Stephen},
 journal = {Science}
}

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