title: Unsupervised corpus distillation for represented indicator measurement on focus species detection.
abstract: The gene ambiguity with the highest dimension is the species with which an entity is associated in biomedical text mining. Furthermore, one of the bottlenecks in gene normalisation is focus species detection. This study presents a method which is robust for all types of articles, particularly those without explicit species mentions. Since our method requires a training corpus, we developed an iterative distillation method to extend the corpus. Unsupervised corpus is therefore helpful for the detection of focus species. In experiments, the proposed method achieved a high accuracy of 85.64% and 84.32% in datasets with and without species mentions respectively.
authors.affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC. p7896116@mail.ncku.edu.tw
authors.forename: Chih-Hsuan
authors.initials: CH
authors.lastname: Wei
authors.affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC.
authors.forename: Hung-Yu
authors.initials: HY
authors.lastname: Kao
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D013045:Species Specificity