IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification - Archive ouverte HAL Access content directly
Conference Papers Year : 2018

IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification

Abstract

This paper describes the systems developed by IRISA to participate to the four tasks of the SMM4H 2018 challenge. For these tweet classification tasks, we adopt a common approach based on recurrent neural networks (BiLSTM). Our main contributions are the use of certain features, the use of Bagging in order to deal with unbalanced datasets, and on the automatic selection of difficult examples. These techniques allow us to reach 91.4, 46.5, 47.8, 85.0 as F1-scores for Tasks 1 to 4.
Fichier principal
Vignette du fichier
SMM4H.pdf (84.49 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01937019 , version 1 (27-11-2018)

Identifiers

  • HAL Id : hal-01937019 , version 1

Cite

Anne-Lyse Minard, Christian Raymond, Vincent Claveau. IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification. SMM4H 2018 - Social Media Mining for Health Applications, Workshop of EMNLP, Oct 2018, Brussels, Belgium. pp.1-2. ⟨hal-01937019⟩
110 View
123 Download

Share

Gmail Mastodon Facebook X LinkedIn More