A document is known by the company it keeps: neighborhood consensus for short text categorization.
During the last decades the Web has become the greatest repository of digital information. In order to organize all this information, several text categorization methods have been developed, achieving accurate results in most cases and in very different domains. Due to the recent usage of Internet a...
| Publicado en: | Language Resources & Evaluation Vol. 47; no. 1; pp. 127 - 150 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Mar2013
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=85873225&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 85873225 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Mar2013 vid: 47 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 85873225 10.1007/s10579-012-9192-1 ppf: 127 ppct: 23 formats: fmt: @attributes: type: P size: 818KB tig: atl: A document is known by the company it keeps: neighborhood consensus for short text categorization. aug: au: Ramírez-de-la-Rosa, Gabriela Montes-y-Gómez, Manuel Solorio, Thamar Villaseñor-Pineda, Luis affil: Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham USA Department of Computational Sciences, National Institute for Astrophysics, Optics and Electronics, Puebla Mexico su: Prototype (Linguistics) World Wide Web Electronic information resources Categorization (Linguistics) Internet users sug: subj: Prototype (Linguistics) World Wide Web Electronic information resources Categorization (Linguistics) Internet users keyword: News titles Prototype-based classification Short text categorization Unlabeled information ab: During the last decades the Web has become the greatest repository of digital information. In order to organize all this information, several text categorization methods have been developed, achieving accurate results in most cases and in very different domains. Due to the recent usage of Internet as communication media, short texts such as news, tweets, blogs, and product reviews are more common every day. In this context, there are two main challenges; on the one hand, the length of these documents is short, and therefore, the word frequencies are not informative enough, making text categorization even more difficult than usual. On the other hand, topics are changing constantly at a fast rate, causing the lack of adequate amounts of training data. In order to deal with these two problems we consider a text classification method that is supported on the idea that similar documents may belong to the same category. Mainly, we propose a neighborhood consensus classification method that classifies documents by considering their own information as well as information about the category assigned to other similar documents from the same target collection. In particular, the short texts we used in our evaluation are news titles with an average of 8 words. Experimental results are encouraging; they indicate that leveraging information from similar documents helped to improve classification accuracy and that the proposed method is especially useful when labeled training resources are limited. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2013. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2013 holdings: @attributes: islocal: N |
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