Abstract
The aim of our work is to develop a flexible and powerful Knowledge Acquisition framework that allows users to rapidly develop Natural Language Processing systems, including information extraction systems. Tasks on which we experimented with our framework are to identify concepts/terms of which positive or negative aspects are mentioned in scientific papers. The results so far are very promising as we managed to build systems with relative ease that achieve F-measures of around 84% on a corpus of scientific papers in the area of artificial intelligence.
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References
Compton, P., Jansen, R.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2, 241–257 (1990)
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© 2004 Springer-Verlag Berlin Heidelberg
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Pham, S.B., Hoffmann, A. (2004). KAFTIE: A New KA Framework for Building Sophisticated Information Extraction Systems. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds) Engineering Knowledge in the Age of the Semantic Web. EKAW 2004. Lecture Notes in Computer Science(), vol 3257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30202-5_45
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DOI: https://doi.org/10.1007/978-3-540-30202-5_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23340-4
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