Encoding and ranking similar Chinese characters

Liu, M; Rus, V; Liao, Q; Liu, L

Encoding and ranking similar Chinese characters

Liu, M

Rus, V Liao, Q Liu, L

Permalink

Publication Type:: Journal Article
Citation:: Journal of Information Science and Engineering, 2017, 33 (5), pp. 1195 - 1211
Issue Date:: 2017-09-01

Closed Access

	Filename	Description	Size
	2f26750d8814fd5edfa770c1e354a15fbd7e.pdf	Published Version	4.79 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, M https://orcid.org/0000-0003-4256-6531	en_US
dc.contributor.author	Rus, V	en_US
dc.contributor.author	Liao, Q	en_US
dc.contributor.author	Liu, L	en_US
dc.date.issued	2017-09-01	en_US
dc.identifier.citation	Journal of Information Science and Engineering, 2017, 33 (5), pp. 1195 - 1211	en_US
dc.identifier.issn	1016-2364	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126704
dc.description.abstract	© 2017 Institute of Information Science. All Rights Reserved. Automatically detecting similar Chinese characters is useful in many areas, such as building intelligent authoring tools (e.g. automatic multiple choice question generation) in the area of computer assisted language learning. Previous work on the computation of Chinese character similarity focused on detecting character glyph similarity while ignored the importance of other character features, such as pronunciation and meaning. In this article, we present a way to encoding 4,500 simplified Chinese characters in terms of character glyph, pronunciation and meaning, annotating similar Chinese characters and automatically ranking similar characters based on the approach of learning to rank. The experiment results indicated that this approach could be useful for ranking and recognizing similar Chinese characters in terms of glyph, pinyin and semantic meaning. Moreover, it has been found that the learning to rank Listwise (ListNet) method was more effective than Pointwise (MART) and Pairwise (RankNet).	en_US
dc.relation.ispartof	Journal of Information Science and Engineering	en_US
dc.relation.isbasedon	10.6688/JISE.2017.33.5.6	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Encoding and ranking similar Chinese characters	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	33	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

© 2017 Institute of Information Science. All Rights Reserved. Automatically detecting similar Chinese characters is useful in many areas, such as building intelligent authoring tools (e.g. automatic multiple choice question generation) in the area of computer assisted language learning. Previous work on the computation of Chinese character similarity focused on detecting character glyph similarity while ignored the importance of other character features, such as pronunciation and meaning. In this article, we present a way to encoding 4,500 simplified Chinese characters in terms of character glyph, pronunciation and meaning, annotating similar Chinese characters and automatically ranking similar characters based on the approach of learning to rank. The experiment results indicated that this approach could be useful for ranking and recognizing similar Chinese characters in terms of glyph, pinyin and semantic meaning. Moreover, it has been found that the learning to rank Listwise (ListNet) method was more effective than Pointwise (MART) and Pairwise (RankNet).

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/126704