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Sensitive Information Detection: Recursive Neural Networks for Encoding Context
We show that our context-based approaches significantly outperforms the family of previous state-of-the-art approaches for sensitive information detection, so-called keyword-based approaches, on real-world data and with human labeled examples of sensitive and non-sensitive documents.
A Real-World Data Resource of Complex Sensitive Sentences Based on Documents from the Monsanto Trial
In this work we present a corpus for the evaluation of sensitive information detection approaches that addresses the need for real world sensitive information for empirical studies.
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