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- W1539773714 abstract "In this paper, we investigate strategies for automatically classifying documents in different languages thematically, geographically or according to other criteria. A novel linguistically motivated text representation scheme is presented that can be used with machine learning algorithms in order to learn classifications from pre-classified examples and then automatically classify documents that might be provided in entirely different languages. Our approach makes use of ontologies and lexical resources but goes beyond a simple mapping from terms to concepts by fully exploiting the external knowledge manifested in such resources and mapping to entire regions of concepts. For this, a graph traversal algorithm is used to explore related concepts that might be relevant. Extensive testing has shown that our methods lead to significant improvements compared to existing approaches." @default.
- W1539773714 created "2016-06-24" @default.
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- W1539773714 date "2007-01-01" @default.
- W1539773714 modified "2023-09-27" @default.
- W1539773714 title "Multilingual Text Classification Using Ontologies" @default.
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- W1539773714 doi "https://doi.org/10.1007/978-3-540-71496-5_49" @default.
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