Leveraging Web Based Evidence Gathering for Drug Information Identification from Tweets

Rupsa Saha, Abir Naskar, Tirthankar Dasgupta, Lipika Dey


Abstract
In this paper, we have explored web-based evidence gathering and different linguistic features to automatically extract drug names from tweets and further classify such tweets into Adverse Drug Events or not. We have evaluated our proposed models with the dataset as released by the SMM4H workshop shared Task-1 and Task-3 respectively. Our evaluation results shows that the proposed model achieved good results, with Precision, Recall and F-scores of 78.5%, 88% and 82.9% respectively for Task1 and 33.2%, 54.7% and 41.3% for Task3.
Anthology ID:
W18-5919
Volume:
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
67–69
Language:
URL:
https://www.aclweb.org/anthology/W18-5919
DOI:
10.18653/v1/W18-5919
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5919.pdf