國立虎尾科技大學 |

Chinese readability analysis using artificial neural networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Chinese readability analysis using artificial neural networks./
作者:	Jeng, Cheng-Chang.
面頁冊數:	1 online resource (202 pages)
附註:	Source: Dissertations Abstracts International, Volume: 63-07, Section: A.
Contained By:	Dissertations Abstracts International63-07A.
標題:	Literacy. -
電子資源:	click for full text (PQDT)
ISBN:	9780493358581

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

Chinese readability analysis using artificial neural networks. - 1 online resource (202 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9780493358581Subjects--Topical Terms:

556453
Literacy.
Subjects--Index Terms:

Artificial neural networksIndex Terms--Genre/Form:

554714
Electronic books.

Chinese readability analysis using artificial neural networks.
LDR:02802ntm a22003977 4500 001 1144927
005 20240617111718.5
006 m o d
007 cr mn ---uuuuu
008 250605s2001 xx obm 000 0 eng d
020 $a 9780493358581
035 $a (MiAaPQ)AAI3023689
035 $a AAI3023689
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Jeng, Cheng-Chang. $3 1470108
245 1 0 $a Chinese readability analysis using artificial neural networks.
264 0 $c 2001
300 $a 1 online resource (202 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Literacy. $3 556453
650 4 $a Reading instruction. $3 1148440
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
650 4 $a Educational technology. $3 556755
653 $a Artificial neural networks
653 $a Chinese
653 $a Readability analysis
655 7 $a Electronic books. $2 local $3 554714
690 $a 0535
690 $a 0710
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Northern Illinois University. $3 1469575
773 0 $t Dissertations Abstracts International $g 63-07A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3023689 $z click for full text (PQDT)