@article{
 title = {A Named Entity Recognition System for Dutch},
 type = {article},
 year = {2002},
 id = {c2b40b4d-b391-39b0-91e7-18cfb9437ca6},
 created = {2011-12-29T19:53:53.000Z},
 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},
 tags = {named entity recognition},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Meulder2002},
 private_publication = {false},
 abstract = {We describe a Named Entity Recognition system for Dutch that combines gazetteers, hand- crafted rules, and machine learning on the basis of seed material. We used gazetteers and a corpus to construct training material for Ripper, a rule learner. Instead of using Ripper to train a complete system, we used many different runs of Ripper in order to derive rules which we then interpreted and implemented in our own, hand-crafted system. This speeded up the building of a hand-crafted system, and allowed us to use many different rule sets in order to improve performance. We discuss the advantages of using machine learning software as a tool in knowledge acquisition, and evaluate the resulting system for Dutch.},
 bibtype = {article},
 author = {Meulder, Fien De and Daelemans, Walter},
 journal = {Context},
 number = {1999}
}

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