Abstract
Traditional Topic Modeling approaches only consider the words in the document. By using an entity-topic modeling approach and including background knowledge about the entities such as the occupation of persons, the location of organizations, the band of a musician etc., we can better cluster related documents together, and produce semantic topic models that can be represented in a knowledge base. In our approach we first reduce the text documents to a set of entities and then enrich this set with background knowledge from DBpedia. Topic modeling is performed on the enriched set of entities and various feature combinations are evaluated in order to determine the combination that achieves the best classification precision or perplexity compared to using word-based topic models alone.
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Acknowledgments
This work has been partially supported by the “InnoProfileTransfer Corporate Smart Content" project funded by the German Federal Ministry of Education and Research (BMBF) and the BMBF Innovation Initiative for the New German Länder - Entrepreneurial Regions.
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Todor, A., Lukasiewicz, W., Athan, T., Paschke, A. (2016). Enriching Topic Models with DBpedia. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_46
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DOI: https://doi.org/10.1007/978-3-319-48472-3_46
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