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    STUDIA INFORMATICA - Issue no. 2 / 2013  
         
  Article:   ON THE STUDY OF REDUCING THE LEXICAL DIFFERENCES BETWEEN SOCIAL KNOWLEDGE SOURCES AND TWITTER FOR TOPIC CLASSIFICATION.

Authors:  ANDREA VARGA.
 
       
         
  Abstract:  

State-of-the-art approaches on cross-source topic classification (TC) of Tweets rely on building a supervised machine learning classifier on Social Knowledge Sources (KSs) (such as DBpedia and Freebase) for detecting topics of Tweets. These approaches typically employ various lexical, syntactical or semantic features derived from the content of these documents or Tweets, often ignoring other indicators to external data sources (e.g. URL), which can provide additional background information for cross-source TC. In order to address these limitations, in this paper we analyse various such indicators, and evaluate their impact on cross-source TC. Our experiments, evaluating the proposed TC in the context of ViolenceDetection (VD) and Emergency Response (ER) tasks, indicate that the Twitter specific information (indicators) contain valuable information; and thus incorporating them into a TC can improve the performance over previous approaches not considering them.

2010 Mathematics Subject Classification. 68T50, 03H65.1998 CR Categories and Descriptors. I.2.7 [Artificial Intelligence]: Natural Language Processing - Text Analysis.

Key words and phrases. cross-source topic classification, linked knowledge sources, violence detection, emergency response.This paper has been presented at the International Conference KEPT2013: Knowledge Engineering Principles and Techniques, organized by Babeș-Bolyai University, Cluj-Napoca, July 5-7 2013.

 
         
     
         
         
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