Title: Improved class-specific vector for biomedical question type classification

Authors: Tanu Gupta; Ela Kumar

Addresses: Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India ' Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India

Abstract: Polysemy is a constant issue in biomedical terms which also affects its QA system. In our work, we consider polysemous words as weak aspect in biomedical question classification and propose two vector model-based solutions that determine the class-specific features of biomedical terms. In first approach, label independent class vector and general word vector are combined using linear compositionality property of vector to generate multiple class-specific embeddings of words. Second is the feature fusion approach, which combines the class-specific sense vector of a word with vectors generated in the first approach. Besides this, a survey dataset COVID-S is introduced in this paper, which is a collection of public queries, myths, and doubts about novel COVID-19 diseases. The series of experiments are performed on two biomedical questions datasets, BioASQ8b and COVID-S, and the results of comparisons with other state-of-arts prove its integrity using SVM and naïve Bayes.

Keywords: class vector; polysemy; biomedical question classification; sense embedding; COVID-S.

DOI: 10.1504/IJCSE.2023.129737

International Journal of Computational Science and Engineering, 2023 Vol.26 No.2, pp.182 - 191

Received: 11 Mar 2021
Accepted: 01 Nov 2021
Published online: 22 Mar 2023 *

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