Abstract
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.
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Basili, R., Giannone, C., Del Vescovo, C., Moschitti, A., Naggar, P. (2009). Kernel-Based Learning for Domain-Specific Relation Extraction. In: Serra, R., Cucchiara, R. (eds) AI*IA 2009: Emergent Perspectives in Artificial Intelligence. AI*IA 2009. Lecture Notes in Computer Science(), vol 5883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10291-2_17
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DOI: https://doi.org/10.1007/978-3-642-10291-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10290-5
Online ISBN: 978-3-642-10291-2
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