Abstract
Techniques for the automatic acquisition of Information Extraction Pattern are still a crucial issue in knowledge engineering. A semi supervised learning method, based on large scale linguistic resources, such as FrameNet and WordNet, is discussed. In particular, a robust method for assigning conceptual relations (i.e. roles) to relevant grammatical structures is defined according to distributional models of lexical semantics over a large scale corpus. Experimental results show that the use of the resulting knowledge base provide significant results, i.e. correct interpretations for about 90% of the covered sentences. This confirms the impact of the proposed approach on the quality and development time of large scale IE systems.
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Basili, R., Croce, D., Giannone, C., De Cao, D. (2010). Acquiring IE Patterns through Distributional Lexical Semantic Models. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2010. Lecture Notes in Computer Science, vol 6008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12116-6_44
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DOI: https://doi.org/10.1007/978-3-642-12116-6_44
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