Thesis Defense: Information Extraction with Neural Networks
Ji Young Lee
Date: Tuesday, May 02, 2017
Time: 4:00 PM to 5:00 PM
Host: Peter Szolovits, Clinical Decision Making Group, CSAIL
Contact: Fern D Keniston, firstname.lastname@example.org
Speaker URL: None
TALK: Thesis Defense: Information Extraction with Neural Networks
Abstract: Electronic health records (EHRs) have been widely adopted, and are a gold mine for clinical research. However, EHRs, especially their text components, remain largely unexplored due to the fact that they must be de-identified prior to any medical investigation. Existing systems for de-identification rely on manual rules or features, which are time-consuming to develop and fine-tune for new datasets. In this thesis, we propose the first de-identification system based on artificial neural networks (ANNs), which achieves state-of-the-art results without any human-engineered features. The ANN architecture is extended to incorporate features, further improving the de-identification performance. Under practical considerations, we explore transfer learning to take advantage of large annotated dataset to improve the performance on datasets with limited number of annotations. The ANN-based system is publicly released as an easy-to-use software package for general purpose named-entity recognition as well as de-identification. Finally, we present an ANN architecture for relation extraction, which ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).
Created by Fern D Keniston at Tuesday, May 02, 2017 at 3:03 PM.