Co-decision matrix framework for name entity recognition in biomedical text.
As a new branch of data mining and knowledge discovery, the research of biomedical text mining has a rapid progress currently. Biomedical named entity (BNE) recognition is a basic technique in the biomedical knowledge discovery and its performance has direct effects on further discovery and processing in biomedical texts. In this paper, we present an improved method based on co-decision matrix framework for Biomedical Named Entity Recognition (BNER). The relativity between classifiers is utilised by using co-decision matrix to exchange decision information among classifiers. The experiments are carried on GENIA corpus with the best result of 75.9% F-score. Experimental results show that the proposed method, co-decision matrix framework, can yield promising performances.
Haochang
H
Wang
Yu
Y
Li
D000465:Algorithms; D035843:Biomedical Research; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D018875:Vocabulary, Controlled