Exploiting context for biomedical entity recognition: From syntax to the web (2004)
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| Venue: | In Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its applications (JNLPBA-2004 |
| Citations: | 17 - 1 self |
BibTeX
@INPROCEEDINGS{Finkel04exploitingcontext,
author = {Jenny Finkel and Shipra Dingare and Huy Nguyen and Malvina Nissim and Christopher Manning and Gail Sinclair},
title = {Exploiting context for biomedical entity recognition: From syntax to the web},
booktitle = {In Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its applications (JNLPBA-2004},
year = {2004}
}
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Abstract
We describe a machine learning system for the recognition of names in biomedical texts. The system makes extensive use of local and syntactic features within the text, as well as external resources including the web and gazetteers. It achieves an Fscore of 70 % on the Coling 2004 NLPBA/BioNLP shared task of identifying five biomedical named entities in the GENIA corpus. 1
Citations
| 422 | Accurate unlexicalized parsing - Klein, Manning - 2003 |
| 101 | Named entity recognition without gazetteers - Mikheev, Moens, et al. - 1999 |
| 65 | Language independent NER using a maximum entropy tagger - JR, Clark |
| 44 | Named entity recognition with character-level models - Klein, Smarr, et al. - 2003 |
| 38 | Description of the LTG system used for MUC-7 - Mikheev, Grover, et al. - 1998 |
| 15 | C.: Exploring the boundaries: Gene and protein identification in biomedical text - Finkel, Dingare, et al. - 2005 |
| 15 | Yuka Tateisi, Hideki Mima, Jun ichi Tsujii, and Jin-Dong Kim. 2002. The GENIA corpus: an annotated research abstract corpus in molecular biology domain - Ohta |
| 8 | Using biological resources to bootstrap text mining. Presentation to the Massachusetts Biotechnology Council Informatics Committee; http://www.e-biosci.org/sept/Hirschman.pdf Copyright © 2005 - Hirschman |
