Unsupervised corpus distillation for represented indicator measurement on focus species detection
by Chih-Hsuan Wei; Hung-Yu Kao
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 8, No. 4, 2013

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.

Online publication date: Mon, 20-Oct-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com