In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. The analysis of transcriptions on investigative activities, such as police interrogatories, for the recognition and storage of complex relations among people and locations is a very difficult and time consuming task, ultimately based on pools of experts. We discuss here an inductive relation extraction platform that opens the way to much cheaper and consistent workflows. The presented empirical investigation shows that accurate results, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting domain-specific requirements. © Springer-Verlag 2009.
CITATION STYLE
Basili, R., Giannone, C., Del Vescovo, C., Moschitti, A., & Naggar, P. (2009). Kernel-based learning for domain-specific relation extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5883 LNAI, pp. 161–171). https://doi.org/10.1007/978-3-642-10291-2_17
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