Impact of Lexical Features on Answer Detection Model in Discussion Forums
Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/2893257 |
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| author | Atif Khan Muhammad Adnan Gul Abdullah Alharbi M. Irfan Uddin Shaukat Ali Bader Alouffi |
| author_facet | Atif Khan Muhammad Adnan Gul Abdullah Alharbi M. Irfan Uddin Shaukat Ali Bader Alouffi |
| author_sort | Atif Khan |
| collection | DOAJ |
| description | Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets. |
| format | Article |
| id | doaj-art-d7cc9954849940fdb1e27c4b18d8b1c7 |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-d7cc9954849940fdb1e27c4b18d8b1c72025-08-20T03:23:51ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/28932572893257Impact of Lexical Features on Answer Detection Model in Discussion ForumsAtif Khan0Muhammad Adnan Gul1Abdullah Alharbi2M. Irfan Uddin3Shaukat Ali4Bader Alouffi5Department of Computer Science, Islamia College Peshawar, Peshawar, KP, PakistanDepartment of Computer Science, Islamia College Peshawar, Peshawar, KP, PakistanDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanDepartment of Computer Science, Islamia College Peshawar, Peshawar, KP, PakistanDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaOnline forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets.http://dx.doi.org/10.1155/2021/2893257 |
| spellingShingle | Atif Khan Muhammad Adnan Gul Abdullah Alharbi M. Irfan Uddin Shaukat Ali Bader Alouffi Impact of Lexical Features on Answer Detection Model in Discussion Forums Complexity |
| title | Impact of Lexical Features on Answer Detection Model in Discussion Forums |
| title_full | Impact of Lexical Features on Answer Detection Model in Discussion Forums |
| title_fullStr | Impact of Lexical Features on Answer Detection Model in Discussion Forums |
| title_full_unstemmed | Impact of Lexical Features on Answer Detection Model in Discussion Forums |
| title_short | Impact of Lexical Features on Answer Detection Model in Discussion Forums |
| title_sort | impact of lexical features on answer detection model in discussion forums |
| url | http://dx.doi.org/10.1155/2021/2893257 |
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