combined svm-crfs for biological named entity recognition with maximal bidirectional squeezing结合生物命名实体识别与svm-crfs最大双向挤压.pdfVIP

Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing Fei Zhu1,2, Bairong Shen1,3,4* 1 Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China, 2 School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China, 3 Institute of Biomedical Information Engineering, Soochow University, Suzhou, Jiangsu, China, 4 Department of Bioinformatics, Medical College, Soochow University, Suzhou, Jiangsu, China Abstract Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs) and conditional random fields (CRFs), which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision