Sat, 26 Jul 2008 08:09:50 BST CiteULike: vlachmore's Tsuruoka http://www.citeulike.org/user/vlachmore/author/Tsuruoka CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/vlachmore/article/2670160 <i>BMC Bioinformatics, Vol. 9, No. Suppl 3. (2008)</i><br /><br />BACKGROUND:One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach.RESULTS:We present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name dictionary built from UMLS.CONCLUSIONS:The experimental results showed that automatically discovered rules can perform comparably to carefully crafted heuristic rules in term mapping tasks, and the computational overhead of rule application is small enough that a very fast implementation is possible. This work will help improve the performance of term-concept mapping tasks in biomedical information extraction especially when good normalization heuristics for the target terminology are not fully known. Normalizing biomedical terms by minimizing ambiguity and variability Yoshimasa Tsuruoka John Mcnaught Sophia Ananiadou doi:10.1186/1471-2105-9-S3-S2 BMC Bioinformatics, Vol. 9, No. Suppl 3. (2008) 2008-04-14T17:56:30-00:00 2008 BMC Bioinformatics 9 Suppl 3 bionlp gene normalization rules http://www.citeulike.org/user/vlachmore/article/777825 <i>(2004)</i> Introduction to the bioentity recognition task at JNLPBA Jin Kim Tomoko Ohta Yoshimasa Tsuruoka Yuka Tateisi Nigel Collier (2004) 2006-07-28T12:52:51-00:00 2004 bionlp entity named recognition shared task http://www.citeulike.org/user/vlachmore/article/1626506 <i>Bioinformatics (12 August 2007)</i><br /><br />MOTIVATION: One of the bottlenecks of biomedical data integration is variation of terms. Exact string matching often fails to associate a name with its biological concept, i.e. ID or accession number in the database, due to seemingly small differences of names. Soft string matching potentially enables us to find the relevant ID by considering the similarity between the names. However, the accuracy of soft matching highly depends on the similarity measure employed. RESULTS: We used logistic regression for learning a string similarity measure from a dictionary. Experiments using several large-scale gene/protein name dictionaries showed that the logistic regression-based similarity measure outperforms existing similarity measures in dictionary look-uptasks. AVAILABILITY: A dictionary look-up system using the similarity measures described in this paper is available at http://text0.mib.man.ac.uk/software/mldic/ CONTACT: yoshimasa.tsuruoka@manchester.ac.uk. Learning string similarity measures for gene/protein name dictionary look-up using logistic regression. Yoshimasa Tsuruoka John McNaught Jun'ichi Tsujii Sophia Ananiadou doi:10.1093/bioinformatics/btm393 Bioinformatics (12 August 2007) 2007-09-06T11:54:19-00:00 2007 Bioinformatics 1460-2059 bionlp gene name normalization http://www.citeulike.org/user/vlachmore/article/1741179 <i>(2004)</i> Proceedings of International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (2004) 2007-10-08T11:03:12-00:00 2004 bionlp conditional crf entity fields named ner random recognition