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Automatically Solving Elementary Science Questions: A Survey.

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Title: Automatically Solving Elementary Science Questions: A Survey.
Authors: Nagdev, Swati1 (AUTHOR) swati.nagdev123@gmail.com, Radke, Mansi A.1 (AUTHOR) mansi.radke@cse.vnit.ac.in, Ramanath, Maya2 (AUTHOR) ramanath@cse.iitd.ac.in
Source: IETE Technical Review. Jan/Feb2023, Vol. 40 Issue 1, p116-135. 20p.
Subjects: School children, Artificial intelligence, RDF (Document markup language), Knowledge graphs
Abstract: Traditionally, Question Answering (QA) has been an important task in the field of Artificial Intelligence (AI) and a substantial amount of research has been done in the past proposing several methods to answer questions in an automated way. The process of QA has been viewed differently by different researchers in the past. Till date, the current / existing systems cannot answer elementary science questions which are obvious and easy to answer even for a primary school student. This paper documents various methodologies proposed in the research area of QA specifically for elementary science domain and includes the discussion of each technique used by various methodologies. We briefly present the overview of three components of generalized QA system and discuss various metrics used for evaluating the QA systems. The paper also presents an exhaustive review of various datasets used for the purpose with their respective description and details. We conclude the paper by identifying the limitations of the methodologies used till date and pointing out some future directions for research in the arena of science QA. [ABSTRACT FROM AUTHOR]
Copyright of IETE Technical Review is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22IETE+Technical+Review%22">IETE Technical Review</searchLink>. Jan/Feb2023, Vol. 40 Issue 1, p116-135. 20p.
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  Data: <searchLink fieldCode="DE" term="%22School+children%22">School children</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22RDF+%28Document+markup+language%29%22">RDF (Document markup language)</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink>
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  Data: Traditionally, Question Answering (QA) has been an important task in the field of Artificial Intelligence (AI) and a substantial amount of research has been done in the past proposing several methods to answer questions in an automated way. The process of QA has been viewed differently by different researchers in the past. Till date, the current / existing systems cannot answer elementary science questions which are obvious and easy to answer even for a primary school student. This paper documents various methodologies proposed in the research area of QA specifically for elementary science domain and includes the discussion of each technique used by various methodologies. We briefly present the overview of three components of generalized QA system and discuss various metrics used for evaluating the QA systems. The paper also presents an exhaustive review of various datasets used for the purpose with their respective description and details. We conclude the paper by identifying the limitations of the methodologies used till date and pointing out some future directions for research in the arena of science QA. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IETE Technical Review is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jan/Feb2023
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