IIIT Hyderabad Publications |
|||||||||
|
Pharmacovigilance from Social Media using Limited Labeled DataAuthor: Shashank Gupta Date: 2019-05-04 Report no: IIIT/TH/2019/22 Advisor:Vasudeva Varma,Manish Gupta AbstractSocial media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for phar- macovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. This poses a significant challenge to the existing Natural Language Processing (NLP) and Information Retrieval (IR) methods. In this thesis, we try to address the problem of “Adverse Drug Reaction (ADR) Mention Extraction”, a specific form of pharamacovigilance, from social media. Specifically, we try to address the problem in the setting where we have a limited access to labeled data but potentially unlimited access to unlabeled data, which is often the case in the real world. We investigate the applicability of two of the popular and well-studied approaches for handling such problems: Semi-supervised learning and Multi-task learning.In the first approach, we propose a novel semi-supervised method which utilizes a novel task of “Unsupervised Drug-Name Prediction”, which is the prediction of drug-name from the tweet context where the actual drug-name is identified and masked from the tweet and the objective is to predict the drug-name from the masked tweet. Enforcing the network to predict the drug-name allows it to learn the context around the drug-name mention. Through experiments we demonstrate that pre-training the network with such task helps in improving performance on the main task. In the second work, we make use of more traditional and well-studied semi-supervised method: co-training, for ADR extraction. To the best of our knowledge, we are the first ones to apply a co-training based methods for entity extraction using deep learning. Through experiments we demonstrate that co-training based network gives a significant improvement in the main task as compared to its fully supervised counterpart. In the third work, we propose a multi-task learning based method which can utilize a similar auxiliary task (adverse drug event detection) to enhance the performance of the main task of our interest, i.e., ADR extraction. Further, in the absence of auxiliary task dataset, we propose a novel joint multi-task learning method to automatically generate weak supervision dataset for the auxiliary task when a large pool of unlabeled tweets is available. Experiments with a large unlabeled pool of tweets, we show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by ∼7.2% in terms of F1 score. Full thesis: pdf Centre for Search and Information Extraction Lab |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |