Automatic generation of short answer questions for reading comprehension assessment.
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| Title: | Automatic generation of short answer questions for reading comprehension assessment. |
|---|---|
| Authors: | HUANG, YAN1 yh358@cam.ac.uk, HE, LIANZHEN2 hlz@zju.edu.cn |
| Source: | Natural Language Engineering. May2016, Vol. 22 Issue 3, p457-489. 33p. |
| Subjects: | Reading comprehension, Writing automation, Lexical-functional grammar, Semantic computing, Paraphrase |
| Abstract: | Writing items for reading comprehension assessment is time-consuming. Automating part of the process can help test-designers to develop assessments more efficiently and consistently. This paper presents an approach to automatically generating short answer questions for reading comprehension assessment. Our major contribution is to introduce Lexical Functional Grammar (LFG) as the linguistic framework for question generation, which enables systematic utilization of semantic and syntactic information. The approach can efficiently generate questions of better quality than previous high-performing question generation systems, and uses paraphrasing and sentence selection to improve the cognitive complexity and effectiveness of questions. [ABSTRACT FROM AUTHOR] |
| Copyright of Natural Language Engineering is the property of Cambridge University Press 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 115125687 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic generation of short answer questions for reading comprehension assessment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22HUANG%2C+YAN%22">HUANG, YAN</searchLink><relatesTo>1</relatesTo><i> yh358@cam.ac.uk</i><br /><searchLink fieldCode="AR" term="%22HE%2C+LIANZHEN%22">HE, LIANZHEN</searchLink><relatesTo>2</relatesTo><i> hlz@zju.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Natural+Language+Engineering%22">Natural Language Engineering</searchLink>. May2016, Vol. 22 Issue 3, p457-489. 33p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Reading+comprehension%22">Reading comprehension</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+automation%22">Writing automation</searchLink><br /><searchLink fieldCode="DE" term="%22Lexical-functional+grammar%22">Lexical-functional grammar</searchLink><br /><searchLink fieldCode="DE" term="%22Semantic+computing%22">Semantic computing</searchLink><br /><searchLink fieldCode="DE" term="%22Paraphrase%22">Paraphrase</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Writing items for reading comprehension assessment is time-consuming. Automating part of the process can help test-designers to develop assessments more efficiently and consistently. This paper presents an approach to automatically generating short answer questions for reading comprehension assessment. Our major contribution is to introduce Lexical Functional Grammar (LFG) as the linguistic framework for question generation, which enables systematic utilization of semantic and syntactic information. The approach can efficiently generate questions of better quality than previous high-performing question generation systems, and uses paraphrasing and sentence selection to improve the cognitive complexity and effectiveness of questions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Natural Language Engineering is the property of Cambridge University Press 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1017/S1351324915000455 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 457 Subjects: – SubjectFull: Reading comprehension Type: general – SubjectFull: Writing automation Type: general – SubjectFull: Lexical-functional grammar Type: general – SubjectFull: Semantic computing Type: general – SubjectFull: Paraphrase Type: general Titles: – TitleFull: Automatic generation of short answer questions for reading comprehension assessment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: HUANG, YAN – PersonEntity: Name: NameFull: HE, LIANZHEN IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 13513249 Numbering: – Type: volume Value: 22 – Type: issue Value: 3 Titles: – TitleFull: Natural Language Engineering Type: main |
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