Kernel-based relation extraction from investigative data

Cristina Giannone, Roberto Basili, Chiara Del Vescovo, Paolo Naggar, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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 dificult 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 workows. 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, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting the specific domain requirements.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series
Number of pages8
Publication statusPublished - 27 Nov 2009
Externally publishedYes
Event3rd Workshop on Analytics for Noisy Unstructured Text Data, AND 2009 - Barcelona, Spain
Duration: 23 Jul 200924 Jul 2009


Other3rd Workshop on Analytics for Noisy Unstructured Text Data, AND 2009


ASJC Scopus subject areas

  • Human-Computer Interaction

Cite this

Giannone, C., Basili, R., Del Vescovo, C., Naggar, P., & Moschitti, A. (2009). Kernel-based relation extraction from investigative data. In ACM International Conference Proceeding Series (pp. 93-100)