An IR-aided machine learning framework for the BioCreative II.5 Challenge.
IEEE/ACM Trans. Comput. Biology Bioinform.（2010）
摘要
The team at the University of Wisconsin-Milwaukee developed an information retrieval and machine learning framework. Our framework requires only the standardized training data and depends upon minimal external knowledge resources and minimal parsing. Within the framework, we built our text mining systems and participated for the first time in all three BioCreative II.5 Challenge tasks. The results show that our systems performed among the top five teams for raw F1 scores in all three tasks and came in third place for the homonym ortholog F1 scores for the INT task. The results demonstrated that our IR-based framework is efficient, robust, and potentially scalable.
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关键词
challenge task,f1 score,minimal parsing,homonym ortholog,learning (artificial intelligence),int task,ir-based framework,information retrieval,systems and software,biocreative ii.5 challenge,bioinformatics genome or protein databases,bioinformatics (genome or protein) databases,biology computing,ir-aided machine learning framework,ir aided machine learning framework,text mining.,biocreative ii,minimal external knowledge resource,standardized training data,text mining systems,medical computing,text analysis,information search and retrieval,bioinformatics,data mining,system performance,training data,databases,text mining,machine learning,learning artificial intelligence,proteins,robustness
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