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Pratheek I
Christ University
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Prediction of Answer Keywords using Char-RNN Pratheek I; Joy Paulose
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (797.121 KB) | DOI: 10.11591/ijece.v9i3.pp2164-2176

Abstract

Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.
Co-Authors Joy Paulose