Ensemble of Convolutional Neural Networks for Medicine Intake Recognition in Twitter




Kai Hakala, Farrokh Mehryary, Hans Moen, Suwisa Kaewphan, Tapio Salakoski, Filip Ginter

Abeed Sarker, Graciela Gonzalez

Social Media Mining for Health Research and Applications

2017

CEUR Workshop Proceedings

Proceedings of the 2nd Social Media Mining for Health Research and Applications Workshop (SMM4H 2017)

CEUR Workshop Proceedings

1996

59

63

5

1613-0073

http://ceur-ws.org/Vol-1996/paper11.pdf

https://research.utu.fi/converis/portal/detail/Publication/28245409



We present the results from our participation in the 2nd Social Media Mining for Health Applications Shared Task –
Task 2. The goal of this task is to develop systems capable of recognizing mentions of medication intake in Twitter.
Our best performing classification system is an ensemble of neural networks with features generated by word- and
character-level convolutional neural network channels and a condensed weighted bag-of-words representation. A
relatively strong performance is achieved, with an F-score of 66.3 according to the official evaluation, resulting in the
5th place in the shared task with performance close to the best systems created by other participating teams.


Last updated on 2024-26-11 at 19:58