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Article: Text classification for cognitive domains: A case using lexical, syntactic and semantic features

TitleText classification for cognitive domains: A case using lexical, syntactic and semantic features
Authors
KeywordsChinese computing
cognitive domain categorisation
text mining
Issue Date2019
PublisherSage Publications Ltd. The Journal's web site is located at http://www.sagepub.co.uk/journal.aspx?pid=105832
Citation
Journal of Information Science, 2019, v. 45 n. 4, p. 516-528 How to Cite?
AbstractVarious automated classifiers have been implemented to categorise learning-related texts into cognitive domains. However, existing studies have applied limited linguistic features, and most have focused on texts written in English, with little attention given to Chinese. This study has tried to fill the gaps by applying a comprehensive set of features that have rarely been used collectively in previous research, with a focus on Chinese analytical texts. Experiments were conducted for classifier learning and evaluation, where a feature selection procedure significantly improved the classification performance. The results showed that different types of features complemented each other in forming strong collective representations of the original texts, and the discriminant nature of the features can be reasonably explained by language usage phenomena. The proposed approach could potentially be applied to other datasets of analytical writings involving cognitive domains, and the text features explored could be reused and further refined in future studies.
Persistent Identifierhttp://hdl.handle.net/10722/275767
ISSN
2023 Impact Factor: 1.8
2023 SCImago Journal Rankings: 0.583
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQIAO, C-
dc.contributor.authorHu, X-
dc.date.accessioned2019-09-10T02:49:17Z-
dc.date.available2019-09-10T02:49:17Z-
dc.date.issued2019-
dc.identifier.citationJournal of Information Science, 2019, v. 45 n. 4, p. 516-528-
dc.identifier.issn0165-5515-
dc.identifier.urihttp://hdl.handle.net/10722/275767-
dc.description.abstractVarious automated classifiers have been implemented to categorise learning-related texts into cognitive domains. However, existing studies have applied limited linguistic features, and most have focused on texts written in English, with little attention given to Chinese. This study has tried to fill the gaps by applying a comprehensive set of features that have rarely been used collectively in previous research, with a focus on Chinese analytical texts. Experiments were conducted for classifier learning and evaluation, where a feature selection procedure significantly improved the classification performance. The results showed that different types of features complemented each other in forming strong collective representations of the original texts, and the discriminant nature of the features can be reasonably explained by language usage phenomena. The proposed approach could potentially be applied to other datasets of analytical writings involving cognitive domains, and the text features explored could be reused and further refined in future studies.-
dc.languageeng-
dc.publisherSage Publications Ltd. The Journal's web site is located at http://www.sagepub.co.uk/journal.aspx?pid=105832-
dc.relation.ispartofJournal of Information Science-
dc.rightsJournal of Information Science. Copyright © Sage Publications Ltd.-
dc.subjectChinese computing-
dc.subjectcognitive domain categorisation-
dc.subjecttext mining-
dc.titleText classification for cognitive domains: A case using lexical, syntactic and semantic features-
dc.typeArticle-
dc.identifier.emailHu, X: xiaoxhu@hku.hk-
dc.identifier.authorityHu, X=rp01711-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1177/0165551518802522-
dc.identifier.scopuseid_2-s2.0-85059683426-
dc.identifier.hkuros302648-
dc.identifier.volume45-
dc.identifier.issue4-
dc.identifier.spage516-
dc.identifier.epage528-
dc.identifier.isiWOS:000474239000007-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0165-5515-

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