Biomedical Named Entity Recognition System

Biomedical Named Entity Recognition System. Patrick, J. & Wang, Y. Entropy, 2005.

Abstract We propose a machine learning approach, using a Maximum Entropy (ME) model to construct a Named Entity Recognition (NER) classifier to retrieve biomedical names from texts. In experiments, we utilize a blend of various linguistic features incorporated into the ME model to assign class labels and location within an entity sequence, and a post- processing strategy for corrections to sequences of tags to produce a state of the art solution. The experimental results on the GENIA corpus achieved an F-score of 68.2% for semantic classification of 23 categories and achieved F-score of 78.1% on identification.

@article{
 title = {Biomedical Named Entity Recognition System},
 type = {article},
 year = {2005},
 keywords = {Information Retrieval,ME model,Named Entity Recognition},
 id = {add1a27e-e97c-3a3d-bb9d-fbc322237aae},
 created = {2011-12-29T19:53:53.000Z},
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 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {named entity recognition},
 read = {false},
 starred = {false},
 authored = {false},
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 citation_key = {Patrick2005},
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 abstract = {Abstract We propose a machine learning approach, using a Maximum Entropy (ME) model to construct a Named Entity Recognition (NER) classifier to retrieve biomedical names from texts. In experiments, we utilize a blend of various linguistic features incorporated into the ME model to assign class labels and location within an entity sequence, and a post- processing strategy for corrections to sequences of tags to produce a state of the art solution. The experimental results on the GENIA corpus achieved an F-score of 68.2% for semantic classification of 23 categories and achieved F-score of 78.1% on identification.},
 bibtype = {article},
 author = {Patrick, Jon and Wang, Yefeng},
 journal = {Entropy}
}

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