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2021 | OriginalPaper | Chapter

Question Answering System Using LSTM and Keyword Generation

Authors : Minakshi Tomer, Manoj Kumar

Published in: Advances in Information Communication Technology and Computing

Publisher: Springer Singapore

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Abstract

Question answering system is an area under natural language processing and information retrieval which automatically answers the questions generated by humans. This work represents an approach for building a system that generates answers for the question based on deep learning neural network which has the competence of processing the information present inside the dataset and enables the user to obtain an insight from the SQuAD dataset by inviting questions in natural language form. Key stages of this approach cover corpus pre-processing, question pre-processing, answer generation, deep neural network for answer extraction and keyword generation. The concept of keyword generation is a novel idea implemented to enable naïve user of the system to apprehend the passage. The system is competent in interpreting the question, responding to the user’s query in natural language form along with generating the keywords. The performance was measured on SQuAD dataset using EM and F1 score.

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Metadata
Title
Question Answering System Using LSTM and Keyword Generation
Authors
Minakshi Tomer
Manoj Kumar
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Information Communication Technology and Computing

Print ISBN: 978-981-15-5420-9

Electronic ISBN: 978-981-15-5421-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-5421-6_28