Support Vector Machine Approach to Extracting Gene References into Function from Biological Documents

In the biological domain, extracting newly discovered functional features from the massive literature is a major challenging issue. To automatically annotate Gene References into Function (GeneRIF) in a new literature is the main goal of this paper. We tried to find GRIF words in a training corpus, and then applied these informative words to annotate the GeneRIFs in abstracts with several different weighting schemes. The experiments showed that the Classic Dice score is at most 50.18%, when the weighting schemes proposed in the paper (Hou et al., 2003) were adopted. In contrast, after employing Support Vector Machines (SVMs) and the definition of classes proposed by Jelier et al. (2003), the score greatly improved to 56.86% for Classic Dice (CD). Adopting the same features, SVMs demonstrated advantage over the Naïve Bayes Classifier. Finally, the combination of the former two models attained a score of 59.51%