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Biomedical NER

Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. Our Bio-NER system addresses three problems faced by ML-based Bio-NER systems: (1) Most ML approaches usually employ singleton features that comprise one linguistic property and at least one class tag. However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. (2) Variations in the numerical parts of biomedical terms cause data sparseness and generate many redundant features. We apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. (3) The assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window. We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing.

webiconDemo site URL: http://asqa.iis.sinica.edu.tw/biocreative2/ 


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Wen-Lian Hsu
Professor, IEEE Fellowbr> Research Fellow
Institute of Information Science ,
Academia Sinica, Taipei,
Taiwan, R. O. C.
Phone:
886-2-27883799 ext.1804
Fax:
886-2-27824814
E-mail: hsu@iis.sinica.edu.tw

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Ting-Yi Sung
Research Fellow
Institute of Information Science ,
Academia Sinica, Taipei,
Taiwan, R. O. C.
Phone:
886-2-27883799 ext.1711
Fax:
886-2-27824814
E-mail:
 tsungiis.sinica.edu.tw

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Intelligent Agent Systems Lab., Institute of Information Science,, Academia Sinica.
128 Academia Road, Sec.2, Nankang, Taipei, Taiwan, ROC
Tel: +886-2-2788-3799, Fax: 886-2-2782-4814, 886-2-2651-8660/div>