Kernel-based relation extraction from investigative data

In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. In the data used on investigative activities, such as police interrogatory or electronic eavesdropping and wiretap, it is customary to find out expressions in non conventional languages as dialects, slangs or coded words. The recognition and storage of complex relations among subjects mentioned in these sources 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. SVMs here are employed to produce a set of possible interpretations for domain relevant concepts. An ontology population process is here realized, where further reasoning can be applied to proof the overall consistency of the extracted information. The empirical investigation presented here shows that accurate results, comparab...

• Type: Journal
• Year: 2009
• Where: AND
• Authors: Cristina Giannone, Roberto Basili, Chiara Del Vescovo, Paolo Naggar, Alessandro Moschitti
• Keywords: AND 2009, Domain Relevant Concepts, Inductive Relation Extraction, Machine Learning, Non Conventional Languages