Thesis Defense: Sequential Short-Text Classification with Neural Networks

Speaker: Franck Dernoncourt , CSAIL Clinical Decision Making Group

Date: Wednesday, May 10, 2017

Time: 3:00 PM to 4:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: Kiva 32-G449

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Host: Peter Szolovits, Clinical Decision Making Group, CSAIL

Contact: Fern D Keniston, fern@csail.mit.edu

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Reminders to: seminars@csail.mit.edu

Reminder Subject: TALK: Thesis Defense: Sequential Short-Text Classification with Neural Networks

Abstract: Medical practice too often fails to incorporate recent medical advances. The two main reasons are that over 25 million scholarly medical articles have been published, and medical practitioners do not have the time to perform literature reviews. Systematic reviews aim at summarizing published medical evidence, but writing them requires tremendous human efforts. In this thesis, we propose several natural language processing methods based on artificial neural networks to facilitate the completion of systematic reviews. In particular, we focus on short-text classification, to help authors of systematic reviews locate the desired information. We introduce several algorithms to perform sequential short-text classification, which outperform state-of-the-art algorithms. To facilitate the choice of hyperparameters, we present a method based on Gaussian processes. Lastly, we release PubMed 20k RCT, a new dataset for sequential sentence classification in randomized control trial abstracts.

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Created by Fern D Keniston at Thursday, May 04, 2017 at 1:05 PM.