An Anonymizable Entity Finder in justice documents
Farzaneh Kazemi (kazemifa <at> iro (point) umontreal (point) ca)
RALI, DIRO [ATTENTION à l'heure plus matinale....]
Wednesday 11 June 2008 at 10:30 AM
Salle 3195, Pavillon André-Aisenstadt
In the Information Age, there is an increasing need to release and share textual data. However, when data contains sensitive or personal information, privacy can only be guaranteed if the sensitive data is anonymized, or de-identified, before its dissemination. Detecting personal information that should be anonymized within a text is a challenging task. We present that machine learning methods can help in detecting anonymizable entities in justice documents. Anonymizable Entity Finder system uses Maximum Entropy model as supervised machine learning approach for a binary classification.
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