Social Semantics and Its Evaluation by Means of Semantic Relatedness and Open Topic Models

Title: Social Semantics and Its Evaluation by Means of Semantic Relatedness and Open Topic Models
Authors: Ulli Waltinger and Alexander Mehler
Pub/Conf:  IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT ’09.

Abstract:
This paper presents an approach using social semantics for the task of topic labelling by means of Open Topic Models. Our approach utilizes a social ontology to create an alignment of documents within a social network. Comprised category information is used to compute a topic generalization. We propose a feature-frequency-based method for measuring semantic relatedness which is needed in order to reduce the number of document features for the task of topic labelling. This method is evaluated against multiple human judgement experiments comprising two languages and three different resources. Overall the results show that social ontologies provide a rich source of terminological knowledge. The performance of the semantic relatedness measure with correlation values of up to .77 are quite promising. Results on the topic labelling experiment show, with an accuracy of up to .79, that our approach can be a valuable method for various NLP applications.

BibTeX:

@inproceedings{1904125,

  author       = {Waltinger, Ulli and Mehler, Alexander},
  isbn         = {978-0-7695-3801-3},
  language     = {English},
  pages        = {42--49},
  publisher    = {IEEE Computer Society},
  series       = {Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on 
                  Web Intelligence and Intelligent Agent Technology},
  title        = {Social Semantics and Its Evaluation by Means of Semantic Relatedness 
                 and Open Topic Models},
  volume       = {1},
  year         = {2009},
}

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