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.
['Ulli Waltinger', 'Alexander Mehler']
Social Semantics and Its Evaluation by Means of Semantic Relatedness and Open Topic Models