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Chinese readability analysis using a...

Jeng, Cheng-Chang.

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Chinese readability analysis using artificial neural networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Chinese readability analysis using artificial neural networks./
Author:	Jeng, Cheng-Chang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2001,
Description:	202 p.
Notes:	Source: Dissertations Abstracts International, Volume: 63-07, Section: A.
Contained By:	Dissertations Abstracts International63-07A.
Subject:	Literacy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3023689
ISBN:	9780493358581

Chinese readability analysis using artificial neural networks.
Jeng, Cheng-Chang.

Chinese readability analysis using artificial neural networks. - Ann Arbor : ProQuest Dissertations & Theses, 2001 - 202 p.

Source: Dissertations Abstracts International, Volume: 63-07, Section: A.

Thesis (Ed.D.)--Northern Illinois University, 2001.

This item must not be sold to any third party vendors.

This study investigated numerous variables that may affect readability and the potential of artificial neural networks (ANN) to estimate Chinese readability while also exploring the creation of more powerful linear models than previous researchers'. To compare the accuracy among four types of methods, the following were compared to each other: a linear regression model proposed in previous research, a linear regression model, ANNs, and human judgments. The results of the comparisons show that the ANN models offered the best estimates among the four models when the comparisons were based on an instrument compiled from 12 official textbooks. Human judgments offered more reliable prediction results and none of the mathematical models achieved good accuracy when the comparisons were based on an instrument compiled from 12 children's books. These results suggest that human judgments are preferable to computational models when unfamiliar readings are involved. Issues discussed include the validity of assigned readability levels and the merits of the various models. Suggestions for future research include adding grammatical variables, using longer passages, and using fuzzy logic in instrument design. Application of the ANN model to English readability is a logical next step. Regardless of language, accurate readability estimates are important to the creation of linguistically suitable instructional materials.

ISBN: 9780493358581Subjects--Topical Terms:

528342
Literacy.
Subjects--Index Terms:

Artificial neural networks

Chinese readability analysis using artificial neural networks.
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100 1 $a Jeng, Cheng-Chang. $3 3766792
245 1 0 $a Chinese readability analysis using artificial neural networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2001
300 $a 202 p.
500 $a Source: Dissertations Abstracts International, Volume: 63-07, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Lockard, James.
502 $a Thesis (Ed.D.)--Northern Illinois University, 2001.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a This study investigated numerous variables that may affect readability and the potential of artificial neural networks (ANN) to estimate Chinese readability while also exploring the creation of more powerful linear models than previous researchers'. To compare the accuracy among four types of methods, the following were compared to each other: a linear regression model proposed in previous research, a linear regression model, ANNs, and human judgments. The results of the comparisons show that the ANN models offered the best estimates among the four models when the comparisons were based on an instrument compiled from 12 official textbooks. Human judgments offered more reliable prediction results and none of the mathematical models achieved good accuracy when the comparisons were based on an instrument compiled from 12 children's books. These results suggest that human judgments are preferable to computational models when unfamiliar readings are involved. Issues discussed include the validity of assigned readability levels and the merits of the various models. Suggestions for future research include adding grammatical variables, using longer passages, and using fuzzy logic in instrument design. Application of the ANN model to English readability is a logical next step. Regardless of language, accurate readability estimates are important to the creation of linguistically suitable instructional materials.
590 $a School code: 0162.
650 4 $a Literacy. $3 528342
650 4 $a Reading instruction. $3 2122756
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
650 4 $a Educational technology. $3 517670
653 $a Artificial neural networks
653 $a Chinese
653 $a Readability analysis
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773 0 $t Dissertations Abstracts International $g 63-07A.
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792 $a 2001
793 $a English
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