Artificial Intelligence has been a major breakthrough in many domains. Now, it has started automating health care domain through Natural Language Processing and Computer Vision applications. As a part of it, researchers are now focusing more on mining health related information from the text shared through social media and clinical trials. This paper explains about our system for health care text classification tasks conducted by Health Language Processing (HLP) Lab. We experimented with representing the target classes available in task 1 and task 2 as vectors. The classification has been performed using Support Vector Machine. To compute the representation for target classes, we used traditional methods available in Vector Space Models and Vector Space Models of Semantics. In this shared task, the task 1 is about distinguishing the tweets mentioning "adverse drug reaction" from the ones which do not. The task 2 is about distinguishing the tweets that includes personal medication intake, possible medication intake and non-intake. The preliminary results are satisfying in-order to continue the research in developing a representation method for target classes.
cited By 0; Conference of 2nd Social Media Mining for Health Research and Applications Workshop, SMM4H 2017 ; Conference Date: 4 November 2017; Conference Code:131897
B. G. H. Balakrishnan, ,, Madasamy, A. K., and Padannayil, S. K., “NLP CEN AMRITA SMM4H: Health care text classification through class embeddings”, in CEUR Workshop Proceedings, 2017, vol. 1996, pp. 79-82.