No abstract

Named entity recognition is a fundamental task in biomedical data mining. Multiple-class annotation is more challenging than singlecla ss annotation. In this paper, we took a single word classification approach to dealing with the multiple-class annotation problem using Support Vector Machines (SVMs). Word attributes, results of existing gene/protein name taggers, context, and other information are important features for classification. During training, the size of training data and the distribution of named entities are considered. The preliminary results showed that the approach might be feasible when more training data is used to alleviate the data imbalance problem.

Named entity (NE) recognition is a fundamental task in biological relationship mining. This paper considers protein/gene collocates extracted from biological corpora as restrictions to enhance the precision rate of protein/gene name recognition. In addition, we integrate the results of multiple NE recognizers to improve the recall rates. Yapex and KeX, and ABGene and Idgene are taken as examples of protein and gene name recognizers, respectively. The precision of Yapex increases from 70.90 to 85.84% at the low expense of the recall rate (i.e., it only decreases 2.44%) when collocates are incorporated. When both filtering and integration strategies are employed together, the Yapex-based integration with KeX shows good performance, i.e., the F-score increases by 7.83% compared to the pure Yapex method. The results of gene recognition show the same tendency. The ABGene-based integration with Idgene shows a 10.18% F-score increase compared to the pure ABGene method. These successful methodologies can be easily extended to other name finders in biological documents.

No abstract