Arabic Question Answering Systems: Gap Analysis
Question-answering (QA) systems aim to provide answers for given questions. The answers can be extracted or generated from either unstructured or structured text. Therefore, QA is considered an important field that can be used to evaluate machine text understanding. Arabic is a challenging language...
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| Veröffentlicht in: | IEEE access 2021, Vol.9, p.63876-63904 |
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| creator | Biltawi, Mariam M. Tedmori, Sara Awajan, Arafat |
| description | Question-answering (QA) systems aim to provide answers for given questions. The answers can be extracted or generated from either unstructured or structured text. Therefore, QA is considered an important field that can be used to evaluate machine text understanding. Arabic is a challenging language for many reasons; although it is spoken by more than 330 million native speakers, research on this language is limited. A few QA systems created for Arabic text are available. They were created to experiment on small datasets, some of which are unavailable. The research on QA systems can be expanded into different components of QA systems, such as question analysis, information retrieval, and answer extraction. The objective of this research is to analyze the QA systems created for Arabic text by reviewing, categorizing, and analyzing the gaps by providing advice to those who would like to work in this field. Six benchmark datasets are available for testing and evaluating Arabic QA systems, and 26 selected Arabic QA systems are analyzed and discussed in this research. |
| doi_str_mv | 10.1109/ACCESS.2021.3074950 |
| format | Article |
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The answers can be extracted or generated from either unstructured or structured text. Therefore, QA is considered an important field that can be used to evaluate machine text understanding. Arabic is a challenging language for many reasons; although it is spoken by more than 330 million native speakers, research on this language is limited. A few QA systems created for Arabic text are available. They were created to experiment on small datasets, some of which are unavailable. The research on QA systems can be expanded into different components of QA systems, such as question analysis, information retrieval, and answer extraction. The objective of this research is to analyze the QA systems created for Arabic text by reviewing, categorizing, and analyzing the gaps by providing advice to those who would like to work in this field. 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Six benchmark datasets are available for testing and evaluating Arabic QA systems, and 26 selected Arabic QA systems are analyzed and discussed in this research.</description><subject>Answer extraction</subject><subject>Arabic question answering</subject><subject>Datasets</subject><subject>Information retrieval</subject><subject>Knowledge based systems</subject><subject>Knowledge discovery</subject><subject>Natural language processing</subject><subject>question analysis</subject><subject>question answering dataset</subject><subject>question answering system</subject><subject>Questions</subject><subject>Search engines</subject><subject>Syntactics</subject><subject>Systems analysis</subject><subject>Task analysis</subject><subject>Unstructured data</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUE1rwkAQDaWFivUXeAn0HN3Z3exHbyFYKwil2J6XyWYjK2rsrlL8942NSOcyw5t5b3gvScZAJgBET4uynK1WE0ooTBiRXOfkLhlQEDpjORP3_-bHZBTjhnSlOiiXg2RaBKy8TT9OLh59u0-Lffxxwe_X6eocj24XX9I5HjoYt-fo41Py0OA2utG1D5Ov19ln-ZYt3-eLslhmlhN1zITTVDCoLXIuiVRCIXQbXXHaWCUdpUBtrRwQnmODOkdwjcqbRopK8pqyYbLodesWN-YQ_A7D2bTozR_QhrXBcPR264ysUbAaQDFbc0TUhDMNWmjHqoYq1Wk991qH0H5ffJpNewqdoWhoDloLSQXvrlh_ZUMbY3DN7SsQcwna9EGbS9DmGnTHGvcs75y7MTQHklPFfgF1n3b8</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Biltawi, Mariam M.</creator><creator>Tedmori, Sara</creator><creator>Awajan, Arafat</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The answers can be extracted or generated from either unstructured or structured text. Therefore, QA is considered an important field that can be used to evaluate machine text understanding. Arabic is a challenging language for many reasons; although it is spoken by more than 330 million native speakers, research on this language is limited. A few QA systems created for Arabic text are available. They were created to experiment on small datasets, some of which are unavailable. The research on QA systems can be expanded into different components of QA systems, such as question analysis, information retrieval, and answer extraction. The objective of this research is to analyze the QA systems created for Arabic text by reviewing, categorizing, and analyzing the gaps by providing advice to those who would like to work in this field. Six benchmark datasets are available for testing and evaluating Arabic QA systems, and 26 selected Arabic QA systems are analyzed and discussed in this research.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2021.3074950</doi><tpages>29</tpages><orcidid>https://orcid.org/0000-0002-7067-5658</orcidid><orcidid>https://orcid.org/0000-0002-4386-0823</orcidid><oa>free_for_read</oa></addata></record> |
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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Answer extraction Arabic question answering Datasets Information retrieval Knowledge based systems Knowledge discovery Natural language processing question analysis question answering dataset question answering system Questions Search engines Syntactics Systems analysis Task analysis Unstructured data |
| title | Arabic Question Answering Systems: Gap Analysis |
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