A4 Refereed article in a conference publication
Ensemble of Convolutional Neural Networks for Medicine Intake Recognition in Twitter
Authors: Kai Hakala, Farrokh Mehryary, Hans Moen, Suwisa Kaewphan, Tapio Salakoski, Filip Ginter
Editors: Abeed Sarker, Graciela Gonzalez
Conference name: Social Media Mining for Health Research and Applications
Publication year: 2017
Journal: CEUR Workshop Proceedings
Book title : Proceedings of the 2nd Social Media Mining for Health Research and Applications Workshop (SMM4H 2017)
Series title: CEUR Workshop Proceedings
Volume: 1996
First page : 59
Last page: 63
Number of pages: 5
ISSN: 1613-0073
Web address : http://ceur-ws.org/Vol-1996/paper11.pdf
Self-archived copy’s web address: 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.
Downloadable publication This is an electronic reprint of the original article. |  |
