Development of Reading Comprehension System for Kannada Text Documents
Anagha Vembar1, DivyaTantri2, Akshita Saxena3, Abhishek Narayanan4, Jagadish S Kallimani5
1Anagha Vembar, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India.
2Divya Tantri, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India.
3Akshita Saxena, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India.
4Abhishek Narayanan, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India.
5Jagadish S Kallimani, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 12 April 2019 | Manuscript Published on 26 July 2019 | PP: 42-45 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F10080486S419/19©BEIESP | DOI: 10.35940/ijitee.F1008.0486S419
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Reading Comprehension (RC) plays an important role in Natural Language Processing (NLP) as it reads and understands text written in Natural Language. Reading Comprehension systems comprehend the given document and answer questions in the context of the given document. This paper proposes a Reading Comprehension System for Kannada documents. The RC system analyses text in the Kannada script and allows users to pose questions to It in Kannada. This system is aimed at masses whose primary language is Kannada – who would otherwise have difficulties in parsing through vast Kannada documents for the information they require. This paper discusses the proposed model built using Term Frequency – Inverse Document Frequency (TF-IDF) and its performance in extracting the answers from the context document. The proposed model captures the grammatical structure of Kannada to provide the most accurate answers to the user.
Keywords: Reading Comprehension, Natural Language Processing, Kannada, Term Frequency- Inverse Document Frequency, Answer Extraction.
Scope of the Article: Computer Science and Its Applications