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Dynamic event type recognition and tagging for data-driven insights in law-enforcement

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Abstract

In law enforcement, investigators are typically tasked with analyzing large collections of evidences in order to identify and extract key information to support investigation cases. In this context, events are key elements that help understanding and reconstructing what happened from the collection of evidence items. With the ever increasing amount of data (e.g., e-mails and content from social media) gathered today as part of investigation tasks (in most part done manually), managing such amount of data can be challenging and prone to missing important details that could be of high relevance to a case. In this paper, we aim to facilitate the work of investigators through a framework for deriving insights from data. We focus on the auto-recognition and dynamic tagging of event types (e.g., phone calls) from (textual) evidence items, and propose a framework to facilitate these tasks and provide support for insights and discovery. The experimental results obtained by applying our approach to a real, legal dataset demonstrate the feasibility of our proposal by achieving good performance in the task of automatically recognizing and tagging event types of interest.

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Notes

  1. https://github.com/berkmancenter/mediacloud-sentence-splitter.

  2. https://nlp.stanford.edu/software/openie.html.

  3. https://nlp.stanford.edu/software/tagger.shtml.

  4. https://spacy.io/api/lemmatizer.

  5. We set an initial threshold to 70%; this parameter can be tuned as needed.

  6. see earlier Footnote 1.

  7. see earlier Footnote 4.

  8. We choose bigrams and trigrams, as based on our observation, averaging the vector of more than 3 words together results in an embedding that is not semantically meaningful.

  9. https://nlp.stanford.edu/software/CRF-NER.html.

  10. https://code.google.com/archive/p/word2vec/.

  11. https://github.com/facebookresearch/fastText.

  12. https://nlp.stanford.edu/projects/glove/.

  13. http://conceptnet.io/.

  14. Term Frequency Inverse Document Frequency.

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Acknowledgements

We acknowledge Data to Decisions CRC (D2D-CRC) for funding this research.

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Authors and Affiliations

  1. UNSW Sydney, Sydney, NSW, 2052, Australia

    Shayan Zamanirad, Boualem Benatallah & Moshe Chai Barukh

  2. Universidad Católica Nuestra Señora de la Asunción, Tte. Cantaluppi y G. Molinas, Asunción, Paraguay

    Carlos Rodriguez

  3. QANTAS Airways, Sydney, NSW, 2020, Australia

    Reza Nouri

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  1. Shayan Zamanirad
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  2. Boualem Benatallah
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  3. Moshe Chai Barukh
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  4. Carlos Rodriguez
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Correspondence to Carlos Rodriguez.

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Carlos Rodríguez, Reza Nouri: This work was done while the authors were at UNSW Sydney.

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Zamanirad, S., Benatallah, B., Barukh, M.C. et al. Dynamic event type recognition and tagging for data-driven insights in law-enforcement. Computing 102, 1627–1651 (2020). https://doi.org/10.1007/s00607-020-00791-z

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