Abstract
We present a novel approach to incorporating semantic information to the problems of natural language processing, in particular to the document classification task. The approach builds on the intuition that semantic relatedness of words can be viewed as a non-static property of the words that depends on the particular task at hand. The semantic relatedness information is incorporated using feature transformations, where the transformations are based on a feature ontology and on the particular classification task and data. We demonstrate the approach on the problem of classifying MEDLINE-indexed documents using the MeSH ontology. The results suggest that the method is capable of improving the classification performance on most of the datasets.
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Ginter, F., Pyysalo, S., Boberg, J., Järvinen, J., Salakoski, T. (2004). Ontology-Based Feature Transformations: A Data-Driven Approach. In: Vicedo, J.L., Martínez-Barco, P., Muńoz, R., Saiz Noeda, M. (eds) Advances in Natural Language Processing. EsTAL 2004. Lecture Notes in Computer Science(), vol 3230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30228-5_25
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DOI: https://doi.org/10.1007/978-3-540-30228-5_25
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