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Verlagsausgabe
DOI: 10.5445/IR/1000089202
Veröffentlicht am 15.01.2019

TECNE: Knowledge based text classification using network embeddings

Türker, Rima,; Koutraki, Maria,; Zhang, Lei,; Sack, Harald

Abstract:
Text classification is an important and challenging task due to its application in various domains such as document organization and news filtering. Several supervised learning approaches have been proposed for text classification. However, most of them require a significant amount of training data. Manually labeling such data can be very time-consuming and costly. To overcome the problem of labeled data, we demonstrate TECNE, a knowledge-based text classification method using network embeddings. The proposed system does not require any labeled training data to classify an arbitrary text. Instead, it relies on the semantic similarity between entities appearing in a given text and a set of predefined categories to determine a category which the given document belongs to.


Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Jahr 2018
Sprache Englisch
Identifikator ISSN: 1613-0073
URN: urn:nbn:de:swb:90-892025
KITopen-ID: 1000089202
Erschienen in 2018 EKAW Posters and Demonstrations Session, EKAW-PD 2018; Nancy; France; 12 November 2018 through 16 November 2018. Ed.: O. Corby
Verlag RWTH, Aachen
Seiten 53-56
Serie CEUR Workshop Proceedings ; 2262
Nachgewiesen in Scopus
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