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. In this paper we present our knowledge acquisition framework, KAFTIE, which strongly supports the rapid development of complex knowledge bases for information extraction. We specifically target scientific papers which involve rather complex sentence structures from which different types of information are automatically extracted. Tasks on which we experimented with our framework are to identify concepts/terms of which positive or negative aspects are mentioned in scientific papers. These tasks are challenging as they require the analysis of the relationship between the concept/term and its sentiment expression. Furthermore, the context of the expression needs to be inspected. 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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Pham, S.B., Hoffmann, A. (2004). Incremental Knowledge Acquisition for Building Sophisticated Information Extraction Systems with KAFTIE. In: Karagiannis, D., Reimer, U. (eds) Practical Aspects of Knowledge Management. PAKM 2004. Lecture Notes in Computer Science(), vol 3336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30545-3_28
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DOI: https://doi.org/10.1007/978-3-540-30545-3_28
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