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

Anne-Lyse Minard 1 Christian Raymond 1 Vincent Claveau 1
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
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.
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01937019
Contributor : Vincent Claveau <>
Submitted on : Tuesday, November 27, 2018 - 7:55:30 PM
Last modification on : Wednesday, December 19, 2018 - 10:34:06 AM

File

SMM4H.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01937019, version 1

Citation

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⟩

Share

Metrics

Record views

41

Files downloads

38