Fine Grained Classification of Personal Data Entities

TL;DR

This paper presents a neural model that enhances fine-grained classification of personal data entities by integrating pattern matching outputs, introducing new data resources, and providing baseline results for improved data discovery.

Contribution

The paper introduces a neural approach that incorporates pattern matching features for detailed personal data entity classification, along with new datasets and resources for research.

Findings

Improved classification accuracy over existing models

New datasets for personal data entity classification

Baseline results for future research

Abstract

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.