Title: A new and efficient method based on syntactic dependency relations features for ad hoc clinical question classification

Authors: Mourad Sarrouti; Abdelmonaime Lachkar

Addresses: Laboratory of Computer Science and Modeling, Department of Computer Science, Faculty of Science Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez, Morocco ' LISA Laboratory, Department of Electrical and Computer Engineering, National School of Applied Sciences, Sidi Mohammed Ben Abdellah University, Fez, Morocco

Abstract: Clinical question classification is an important and a challenging task for any clinical Question Answering (QA) system. It classifies questions into different semantic categories, which indicate the expected semantic type of answers. Indeed, the semantic category allows filtering out irrelevant answer candidates. Existing methods dealing with the problem of clinical question classification don't take into account the syntactic dependency relations in questions. Therefore, this may impact negatively the performance of the clinical question classification system. To overcome this drawback, we propose to incorporate the syntactic dependency relations as discriminative features for machine learning. To evaluate and illustrate the interest of our contribution, we conduct a comparative study using nine methods and two machine-learning algorithms: Naïve Bayes and Support Vector Machine (SVM). The obtained results using 4654 clinical questions maintained by the National Library of Medicine (NLM) show that our proposed method is very efficient and outperforms greatly the others by the average F-score of 4.5% for Naïve Bayes and 4.73% for SVM.

Keywords: clinical questions; question answering systems; clinical question classification; machine learning; syntactic dependency relations; natural language processing; NLP; bioinformatics; semantics; naive Bayes; support vector machines; SVM.

DOI: 10.1504/IJBRA.2017.083150

International Journal of Bioinformatics Research and Applications, 2017 Vol.13 No.2, pp.161 - 177

Received: 23 Nov 2015
Accepted: 07 Aug 2016

Published online: 06 Mar 2017 *

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