Kaoru Yamamoto
ABSTRACT
We explore the use of morphological analysis as preprocessing for protein name tagging. Our method finds protein names by chunking based on a morpheme, the smallest unit determined by the morphological analysis. This helps to recognize the exact boundaries of protein names. Moreover, our morphological analyzer can deal with compounds. This offers a simple way to adapt name descriptions from biomedical resources for language processing. Using GENIA corpus 3.01, our method attains f-score of 70 points for protein molecule names, and 75 points for protein names including molecules, families and domains.
- B. Boeckmann, A. Bairoch, R. Apweiler, M.-C. Blatter, A. Estreicher, E. Gasteiger, M. J. Martin, K. Michoud, C. O'Donovan, I. Phan, S. Pilbout, and M. Schneider. 2003. The SWISS-PROT protein knowledgebase and its supplement TrEMBL. Nucleic Acids Res., 31:365--370.Google Scholar
Cross Ref
- N. Collier, C. Nobata, and J. Tsujii. 2000. Extracting the Names of Genes and Gene Products with a Hidden Markov Model. COLING, pages 201--207. Google ScholarDigital Library
- M. Collins and Y. Singer. 1999. Unsupervised Models for Named Entity Classification. EMNLP-VLC, pages 100--110.Google Scholar
- The Gene Ontology Consortium. 2000. Gene ontology: tool for the unification of biology. Nature Genetics, 25:25--29.Google ScholarCross Ref
- K. Fukuda, T. Tsunoda, A. Tamura, and T. Takagi. 1998. Toward information extraction: identifying protein names from biological papers. PSB, pages 705--716.Google Scholar
- D. Hanisch, J. Fluck, HT. Mevissen, and R. Zimmer. 2003. Playing biology's name game: identifying protein names in scientific text. PSB, pages 403--414.Google Scholar
- J. Kazama, T. Makino, Y. Ohta, and J. Tsujii. 2002. Tuning Support Vector Machines for Biomedical Named Entity Recognition. ACL Workshop on NLP in Biomedical Domain, pages 1--8. Google ScholarDigital Library
- T. Kudo and Y. Matsumoto. 2001. Chunking with Support Vector Machines. NAACL, pages 192--199.Google Scholar
- C. D. Manning and Schütze. 1999. Foundations of Statistical Natural Language Processing. The MIT Press. Google ScholarDigital Library
- NLM. 2002. UMLS Knowledge Sources. 13th edition.Google Scholar
- F. Olsson, G. Eriksson, K. Franzen, L. Asker, and P. Lidén. 2002. Notions of Correctness when Evaluating Protein Name Tagger. COLING, pages 765--771.Google Scholar
- L. Tanabe and W. J. Wilbur. 2002. Tagging gene and protein names in biomedical text. Bioinformatics, 18(8):1124--1132.Google ScholarCross Ref
- E. F. Tjong Kim Sang and J. Veenstra. 1999. Representing Text Chunks. EACL, pages 173--179. Google ScholarDigital Library
- C. H. Wu, H. Huang, L. Arminski, J. Castro-Alvear, Y. Chen, Z.-Z. Hu, R. S. Ledley, K. C. Lewis, H.-W. Mewes, B. C. Orcutt, B. E. Suzek, A. Tsugita, C. R. Vinayaka, L.-S. L. Yeh, J. Zhang, and W. C. Barker. 2002. The Protein Information Resource: an integrated public resource of functional annotation of proteins. Nucleic Acids Res., 30:35--37.Google ScholarCross Ref
- T. Yamashita and Y. Matsumoto. 2000. Language Independent Morphological Analysis. 6th Applied Natural Language Processing Conference, pages 232--238. Google ScholarDigital Library
- Protein name tagging for biomedical annotation in text
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