Prediction of Answer Keywords using Char-RNN

Title

Prediction of Answer Keywords using Char-RNN

Creator

Pratheek I.; Paulose J.

Description

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. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved.

Source

International Journal of Electrical and Computer Engineering, Vol-9, No. 3, pp. 2164-2176.

Date

2019-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Gated Recurrent Units; Long Short Term Memory; Natural Language Processing; Neural Networks; Recurrent Neural Network

Coverage

Pratheek I., Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India; Paulose J., Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 20888708

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijece.v9i3.pp2164-2176

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