Abstract
The paper presents a co-occurrence based approach to extracting semantic relations from text. We concentrate on Semantic Relations as relations among concepts, and instances of such relations, as used in taxonomies and ontologies. We show how typed semantic relations can be derived from association networks by filters based on linguistic and non-linguistic knowledge. The main point of the paper is to argue that there is no single step derivation of knowledge about semantic relations. Learning semantic relations from text requires linguistic and non-linguistic knowledge sources of different kinds and quality that need to iteratively interact in order to derive high quality knowledge about semantic relations.
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Heyer, G. (2011). Learning Semantic Relations from Text. In: Mehler, A., Kühnberger, KU., Lobin, H., Lüngen, H., Storrer, A., Witt, A. (eds) Modeling, Learning, and Processing of Text Technological Data Structures. Studies in Computational Intelligence, vol 370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22613-7_16
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DOI: https://doi.org/10.1007/978-3-642-22613-7_16
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