Enhanced lexicon based models for extracting question-answer pairs from web forum

A Web forum is an online community that brings people in different geographical locations together. Members of the forum exchange ideas and expertise. As a result, a huge amount of contents on different topics are generated on a daily basis. The huge human generated contents of web forum can be mine...

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Main Author:	Obasa, Adekunle Isiaka
Format:	Thesis
Language:	English
Published:	2016
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/79067/1/AdekunleIsiakaObasaPFC2016.pdf
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id	my-utm-ep.79067
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spelling	my-utm-ep.790672018-09-27T06:07:14Z Enhanced lexicon based models for extracting question-answer pairs from web forum 2016 Obasa, Adekunle Isiaka QA75 Electronic computers. Computer science A Web forum is an online community that brings people in different geographical locations together. Members of the forum exchange ideas and expertise. As a result, a huge amount of contents on different topics are generated on a daily basis. The huge human generated contents of web forum can be mined as questionanswer pairs (Q&A). One of the major challenges in mining Q&A from web forum is to establish a good relationship between the question and the candidate answers. This problem is compounded by the noisy nature of web forum's human generated contents. Unfortunately, the existing methods that are used to mine knowledge from web forums ignore the effect of noise on the mining tools, making the lexical contents less effective. This study proposes lexicon based models that can automatically mine question-answer pairs with higher accuracy scores from web forum. The first phase of the research produces question mining model. It was implemented using features generated from unigram, bigram, forum metadata and simple rules. These features were screened using both chi-square and wrapper techniques. Wrapper generated features were used by Multinomial Naïve Bayes to finally build the model. The second phase produced a normalized lexical model for answer mining. It was implemented using 13 lexical features that cut across four quality dimensions. The performance of the features was enhanced by noise normalization, a process that fixed orthographic, phonetic and acronyms noises. The third phase of the research produced a hybridized model of lexical and non-lexical features. The average performances of the question mining model, normalized lexical model and hybridized model for answer mining were 90.3%, 97.5%, and 99.5% respectively on three data sets used. They outperformed all previous works in the domain. The first major contribution of the study is the development of an improved question mining model that is characterized by higher accuracy, better specificity, less complex and ability to generate good accuracy across different forum genres. The second contribution is the development of normalized lexical based model that has capability to establish good relationship between a question and its corresponding answer. The third contribution is the development of a hybridized model that integrates lexical features that guarantee relevance with non-lexical that guarantee quality to mine web forum answers. The fourth contribution is a novel integration of question and answer mining models to automatically generate question-answer pairs from web forum. 2016 Thesis http://eprints.utm.my/id/eprint/79067/ http://eprints.utm.my/id/eprint/79067/1/AdekunleIsiakaObasaPFC2016.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Obasa, Adekunle Isiaka Enhanced lexicon based models for extracting question-answer pairs from web forum
description	A Web forum is an online community that brings people in different geographical locations together. Members of the forum exchange ideas and expertise. As a result, a huge amount of contents on different topics are generated on a daily basis. The huge human generated contents of web forum can be mined as questionanswer pairs (Q&A). One of the major challenges in mining Q&A from web forum is to establish a good relationship between the question and the candidate answers. This problem is compounded by the noisy nature of web forum's human generated contents. Unfortunately, the existing methods that are used to mine knowledge from web forums ignore the effect of noise on the mining tools, making the lexical contents less effective. This study proposes lexicon based models that can automatically mine question-answer pairs with higher accuracy scores from web forum. The first phase of the research produces question mining model. It was implemented using features generated from unigram, bigram, forum metadata and simple rules. These features were screened using both chi-square and wrapper techniques. Wrapper generated features were used by Multinomial Naïve Bayes to finally build the model. The second phase produced a normalized lexical model for answer mining. It was implemented using 13 lexical features that cut across four quality dimensions. The performance of the features was enhanced by noise normalization, a process that fixed orthographic, phonetic and acronyms noises. The third phase of the research produced a hybridized model of lexical and non-lexical features. The average performances of the question mining model, normalized lexical model and hybridized model for answer mining were 90.3%, 97.5%, and 99.5% respectively on three data sets used. They outperformed all previous works in the domain. The first major contribution of the study is the development of an improved question mining model that is characterized by higher accuracy, better specificity, less complex and ability to generate good accuracy across different forum genres. The second contribution is the development of normalized lexical based model that has capability to establish good relationship between a question and its corresponding answer. The third contribution is the development of a hybridized model that integrates lexical features that guarantee relevance with non-lexical that guarantee quality to mine web forum answers. The fourth contribution is a novel integration of question and answer mining models to automatically generate question-answer pairs from web forum.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Obasa, Adekunle Isiaka
author_facet	Obasa, Adekunle Isiaka
author_sort	Obasa, Adekunle Isiaka
title	Enhanced lexicon based models for extracting question-answer pairs from web forum
title_short	Enhanced lexicon based models for extracting question-answer pairs from web forum
title_full	Enhanced lexicon based models for extracting question-answer pairs from web forum
title_fullStr	Enhanced lexicon based models for extracting question-answer pairs from web forum
title_full_unstemmed	Enhanced lexicon based models for extracting question-answer pairs from web forum
title_sort	enhanced lexicon based models for extracting question-answer pairs from web forum
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2016
url	http://eprints.utm.my/id/eprint/79067/1/AdekunleIsiakaObasaPFC2016.pdf
_version_	1747818138949386240

Enhanced lexicon based models for extracting question-answer pairs from web forum

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