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Computational Intelligence and Neuroscience - Volume 2016 2016, Article ID 3483528, 16 pages -

Research Article

Faculty of Computer Science, Universitas Mercu Buana, l. Meruya Selatan No. 1, Kembangan, Jakarta Barat 11650, Indonesia

Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, West Java 16424, Indonesia

Received 27 May 2016; Revised 8 August 2016; Accepted 18 September 2016

Academic Editor: Trong H. Duong

Copyright © 2016 Mujiono Sadikin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance less than 0.75. This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645.





Author: Mujiono Sadikin, Mohamad Ivan Fanany, and T. Basaruddin

Source: https://www.hindawi.com/



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