Question answering using evolving networks

This study involves the exploration of evolving networks as a viable machine learning approach for question answering systems. Question answering systems engage in analyzing a free text, accepting questions and giving answers based on the input text. There are various works involving question answer...

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Main Authors: See, Solomon Lim, Sih, Marc S., Tacderas, Beehjae F., Teo, Michael G.
Format: text
Language:English
Published: Animo Repository 2004
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/14111
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Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etd_bachelors-14753
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-147532021-11-08T03:26:27Z Question answering using evolving networks See, Solomon Lim Sih, Marc S. Tacderas, Beehjae F. Teo, Michael G. This study involves the exploration of evolving networks as a viable machine learning approach for question answering systems. Question answering systems engage in analyzing a free text, accepting questions and giving answers based on the input text. There are various works involving question answering systems using rule-based approaches and machine learning approaches. This research extends the work on question answering using machine-learning approaches by using evolving networks. The basic idea of evolving networks to improve performance. Three evolving network approaches on a back-propagation neural network were explored, namely, weights evolution, learning parameters evolution, and architecture evolution. The accuracy of each evolving network approach was benchmarked vis-a-vis other related works on question answering systems and was found to yield performance that is at par with the best performing approaches and in some instances, incrementally better. Thus, the evolving network approach is found to be a viable and competitive machine learning approach for question answering systems. 2004-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/14111 Bachelor's Theses English Animo Repository Machine learning Algorithms Question-answering systems Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Machine learning
Algorithms
Question-answering systems
Computer Sciences
spellingShingle Machine learning
Algorithms
Question-answering systems
Computer Sciences
See, Solomon Lim
Sih, Marc S.
Tacderas, Beehjae F.
Teo, Michael G.
Question answering using evolving networks
description This study involves the exploration of evolving networks as a viable machine learning approach for question answering systems. Question answering systems engage in analyzing a free text, accepting questions and giving answers based on the input text. There are various works involving question answering systems using rule-based approaches and machine learning approaches. This research extends the work on question answering using machine-learning approaches by using evolving networks. The basic idea of evolving networks to improve performance. Three evolving network approaches on a back-propagation neural network were explored, namely, weights evolution, learning parameters evolution, and architecture evolution. The accuracy of each evolving network approach was benchmarked vis-a-vis other related works on question answering systems and was found to yield performance that is at par with the best performing approaches and in some instances, incrementally better. Thus, the evolving network approach is found to be a viable and competitive machine learning approach for question answering systems.
format text
author See, Solomon Lim
Sih, Marc S.
Tacderas, Beehjae F.
Teo, Michael G.
author_facet See, Solomon Lim
Sih, Marc S.
Tacderas, Beehjae F.
Teo, Michael G.
author_sort See, Solomon Lim
title Question answering using evolving networks
title_short Question answering using evolving networks
title_full Question answering using evolving networks
title_fullStr Question answering using evolving networks
title_full_unstemmed Question answering using evolving networks
title_sort question answering using evolving networks
publisher Animo Repository
publishDate 2004
url https://animorepository.dlsu.edu.ph/etd_bachelors/14111
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