Presentation 2001/10/9
Reading-Time Estimation Using Neural Network
Ryo NAGATA, Fumito MASUI, Atsuo KAWAI, Tsutomu SHIINO,
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Abstract(in English) In reading, readability is crucial. Likewise readbility can also take an impotant role in processing texts. For example, materials for teaching or presentation could easily be prepared if readability is known beforehand. However, there have been few methods to rate readability quantitatively since it is abstract and depends on the person who reads the texts. Furthermore factors that decide whether texts are easy or difficult vary. Thus, the various factors should be considered to evaluate texts. In this paper, we propose a method to rate readability quantitatively using Reading-Speed that is often used in the field of English education and oral translation. Firstly, we measured several Reading-Speeds that examinees provided and extract features from the analyzed texts used in the measurement. Secondary, we examine the relation between Reading-Speed and the features using neural network. The result shows that after learning 20 texts Reading-Speed can be estimated and used as an index of readability.
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Keyword(in English) Reading-Speed / Reading-Time / Readability / Natural Language Processing
Paper # TL2001-15
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Committee TL
Conference Date 2001/10/9(1days)
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Registration To Thought and Language (TL)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Reading-Time Estimation Using Neural Network
Sub Title (in English)
Keyword(1) Reading-Speed
Keyword(2) Reading-Time
Keyword(3) Readability
Keyword(4) Natural Language Processing
1st Author's Name Ryo NAGATA
1st Author's Affiliation Faculty of Engineering, Mie University()
2nd Author's Name Fumito MASUI
2nd Author's Affiliation Faculty of Engineering, Mie University
3rd Author's Name Atsuo KAWAI
3rd Author's Affiliation Faculty of Engineering, Mie University
4th Author's Name Tsutomu SHIINO
4th Author's Affiliation Faculty of Engineering, Mie University
Date 2001/10/9
Paper # TL2001-15
Volume (vol) vol.101
Number (no) 349
Page pp.pp.-
#Pages 6
Date of Issue