Performance Evaluation of a Natural Language Processing approach applied in White Collar crime investigation

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Title: Performance Evaluation of a Natural Language Processing approach applied in White Collar crime investigation
Authors: van Banerveld, Maarten
Le-Khac, Nhien-An
Kechadi, Tahar
Permanent link: http://hdl.handle.net/10197/6137
Date: Sep-2014
Abstract: In today's world we are confronted with increasing amounts of information every day coming from a large variety of sources. People and corporations are producing data on a large scale, and since the rise of the internet, e-mail and social media the amount of produced data has grown exponentially. From a law enforcement perspective we have to deal with these huge amounts of data when a criminal investigation is launched against an individual or company. Relevant questions need to be answered like who committed the crime, who were involved, what happened and on what time,who were communicating and about what? Not only the amount of available data to investigate has increased enormously, but also the complexity of this data has increased. When these communication patterns need to be combined with for instance a seized financial administration or corporate document shares a complex investigation problem arises. Recently, criminal investigators face a huge challenge when evidence of a crime needs to be found in the Big Data environment where they have to deal with large and complex datasets especially in financial and fraud investigations. To tackle this problem, a financial and fraud investigation unit of a European country has developed a new tool named LES that uses Natural Language Processing (NLP) techniques to help criminal investigators handle large amounts of textual information in a more efficient and faster way. In this paper, we present briefly this tool and we focus on the evaluation its performance in terms of the requirements of forensic investigation: speed, smarter and easier for investigators. In order to evaluate this LES tool, we use different performance metrics. We also show experimental results of our evaluation with large and complex datasets from real-world application.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Volume: 8860
Issue: 2014
Start page: 29
End page: 43
Copyright (published version): 2014 Springer International Publishing Switzerland
Keywords: Machine Learning & StatisticsBig dataNatural language processingFinancial and fraud investigationHadoop/MapReduce
DOI: 10.1007/978-3-319-12778-1_3
Language: en
Status of Item: Peer reviewed
Conference Details: Future Data and Security Engineering, 1st International Conference on Future Data and Security Engineering 2014 (FDSE 2014), Springer Verlag LNCS, HoChiMinh City, Vietnam,19-21 November 2014
ISBN: 9783319127774
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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