Classifying unlabeled short texts using a fuzzy declarative approach.
Web 2.0 provides user-friendly tools that allow persons to create and publish content online. User generated content often takes the form of short texts (e.g., blog posts, news feeds, snippets, etc). This has motivated an increasing interest on the analysis of short texts and, specifically, on their...
| Published in: | Language Resources & Evaluation Vol. 47; no. 1; pp. 151 - 179 |
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| Main Authors: | , , , , |
| Format: | Article |
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Springer Nature
Mar2013
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=85873227&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 85873227 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: 85873227 10.1007/s10579-012-9203-2 ppf: 151 ppct: 28 formats: fmt: @attributes: type: P size: 836KB tig: atl: Classifying unlabeled short texts using a fuzzy declarative approach. aug: au: Romero, Francisco Julián-Iranzo, Pascual Soto, Andrés Ferreira-Satler, Mateus Gallardo-Casero, Juan affil: Department of Information Technologies and Systems, University of Castilla La Mancha, Paseo de la Universidad, 4 13071 Ciudad Real Spain Department of Computer Science, Universidad Autònoma del Carmen, Ciudad del Carmen CP 24160 Campeche Mèxico su: Categorization (Linguistics) Linguistic analysis Ontologies (Information retrieval) Thesauri Subject headings sug: subj: Categorization (Linguistics) Linguistic analysis Ontologies (Information retrieval) Thesauri Subject headings keyword: Ontologies Text categorization Unlabeled short texts ab: Web 2.0 provides user-friendly tools that allow persons to create and publish content online. User generated content often takes the form of short texts (e.g., blog posts, news feeds, snippets, etc). This has motivated an increasing interest on the analysis of short texts and, specifically, on their categorisation. Text categorisation is the task of classifying documents into a certain number of predefined categories. Traditional text classification techniques are mainly based on word frequency statistical analysis and have been proved inadequate for the classification of short texts where word occurrence is too small. On the other hand, the classic approach to text categorization is based on a learning process that requires a large number of labeled training texts to achieve an accurate performance. However labeled documents might not be available, when unlabeled documents can be easily collected. This paper presents an approach to text categorisation which does not need a pre-classified set of training documents. The proposed method only requires the category names as user input. Each one of these categories is defined by means of an ontology of terms modelled by a set of what we call proximity equations. Hence, our method is not category occurrence frequency based, but highly depends on the definition of that category and how the text fits that definition. Therefore, the proposed approach is an appropriate method for short text classification where the frequency of occurrence of a category is very small or even zero. Another feature of our method is that the classification process is based on the ability of an extension of the standard Prolog language, named , for flexible matching and knowledge representation. This declarative approach provides a text classifier which is quick and easy to build, and a classification process which is easy for the user to understand. The results of experiments showed that the proposed method achieved a reasonably useful performance. 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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