@inProceedings{
 title = {Using markov models for named entity recognition in German newspapers},
 type = {inProceedings},
 year = {2002},
 pages = {29–37},
 websites = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.8562&rep=rep1&type=pdf},
 publisher = {Citeseer},
 id = {93de4aff-1b3b-35f7-8d55-836b2773ec2c},
 created = {2012-01-21T12:35:31.000Z},
 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 = {Rossler2002},
 private_publication = {false},
 abstract = {This paper describes preliminary experiments for a system of named entity recognition in German newspapers. The approach is based on second order Markov Models trained on a tagged corpus. No gazetteers are used, only a list of words providing evidence is integrated. These words are extracted by statistical methods from an annotated corpus. The input basically consists of a part of speech tagged text, except the words occurring in the gained list, which replace the tags with their word form. The experiments investigate in how far such a limited approach is suitable for German and show that it provides some evidence. However, of course, it has to be enhanced.},
 bibtype = {inProceedings},
 author = {Rössler, Marc},
 booktitle = {Proceedings of the Workshop on Machine Learning Approaches in Computational Linguistics}
}

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