Tree-LSTM and Cross-Corpus Training for Extracting Biomedical Relationships from Text

Joël Legrand 1 Yannick Toussaint 1 Chedy Raïssi 1 Adrien Coulet 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : A key aspect of machine learning-based relationship extraction algorithms is the availability of training data. Manually annotated corpora are valuable resources for this task, but the time and expertise required for their development explain that still few corpora are available. For tasks related to precision medicine, most of them are rather small (i.e., hundreds of sentences) or focus on specialized relationships (e.g., drug-drug interactions) that rarely fit what one wants to extract. In this paper, we experiment Tree-LSTM, to extract relationships from biomedical texts with high performance. In addition we show that relatively large corpora, even when focusing on a distinct type of relationships, can be use to improve the performance of deep learning-based system for relationship extraction tasks for which initial resources are scarce.
Type de document :
Communication dans un congrès
DLPM2017 Workshop - 2nd International Workshop on Deep Learning for Precision Medicine, Sep 2017, Skopje, Macedonia
Liste complète des métadonnées

Littérature citée [28 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01592802
Contributeur : Joël Legrand <>
Soumis le : lundi 25 septembre 2017 - 14:13:23
Dernière modification le : jeudi 11 janvier 2018 - 06:25:24
Document(s) archivé(s) le : mardi 26 décembre 2017 - 13:31:31

Fichier

tree-lstm-cross.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01592802, version 1

Citation

Joël Legrand, Yannick Toussaint, Chedy Raïssi, Adrien Coulet. Tree-LSTM and Cross-Corpus Training for Extracting Biomedical Relationships from Text. DLPM2017 Workshop - 2nd International Workshop on Deep Learning for Precision Medicine, Sep 2017, Skopje, Macedonia. 〈hal-01592802〉

Partager

Métriques

Consultations de la notice

376

Téléchargements de fichiers

251