Fine Grained Classification of Personal Data Entities
This work addresses the need for improved personal data entity classification in compliance contexts, but it is incremental as it builds on existing pattern matching systems.
The paper tackles the problem of fine-grained classification of personal data entities, such as for GDPR and HIPAA compliance, by proposing a neural model that uses pattern matching outputs as features and achieves baseline results on new datasets.
Entity Type Classification can be defined as the task of assigning category labels to entity mentions in documents. While neural networks have recently improved the classification of general entity mentions, pattern matching and other systems continue to be used for classifying personal data entities (e.g. classifying an organization as a media company or a government institution for GDPR, and HIPAA compliance). We propose a neural model to expand the class of personal data entities that can be classified at a fine grained level, using the output of existing pattern matching systems as additional contextual features. We introduce new resources, a personal data entities hierarchy with 134 types, and two datasets from the Wikipedia pages of elected representatives and Enron emails. We hope these resource will aid research in the area of personal data discovery, and to that effect, we provide baseline results on these datasets, and compare our method with state of the art models on OntoNotes dataset.