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Knowledge-Based Dataless Text Categorization

  • Rima TürkerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11762)

Abstract

Text categorization is an important task due to the rapid growth of online available text data in various domains such as web search snippets, news documents, etc. Traditional supervised methods require a significant amount of training data and manually labeling such data can be very time-consuming and costly. Moreover, in case the text to be labeled is of a specific domain, then only the expensive domain experts are able to fulfill the manual labeling task. This thesis focuses on the problem of missing labeled data and aims to develop a novel and generic model which does not require any labeled training data to categorize text. Instead, it utilizes the semantic similarity between documents and the predefined categories by leveraging graph embedding techniques.

Keywords

Text categorization Dataless classification Network embeddings 

Notes

Acknowledgement

This thesis is supervised by Prof. Harald Sack and Dr. Lei Zhang.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.FIZ Karlsruhe, Leibniz Institute for Information InfrastructureEggenstein-LeopoldshafenGermany
  2. 2.AIFBKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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