Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining

Abstract : This paper deals with the extraction of semantic relations from scientific texts. Pattern-based representations are compared to word embeddings in unsupervised clustering experiments, according to their potential to discover new types of semantic relations and recognize their instances. The results indicate that sequential pattern mining can significantly improve pattern-based representations, even in a completely unsupervised setting.
Type de document :
Communication dans un congrès
XVIth Symposium on Intelligent Data Analysis, Oct 2016, Stockholm, Sweden. pp.237 - 248, 2016, 〈10.1007/978-3-319-46349-0_21〉
Liste complète des métadonnées

Littérature citée [38 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01405041
Contributeur : Kata Gabor <>
Soumis le : mardi 29 novembre 2016 - 14:28:19
Dernière modification le : jeudi 11 janvier 2018 - 06:23:10
Document(s) archivé(s) le : lundi 27 mars 2017 - 08:38:59

Fichier

unsupervised-relation-extracti...
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Kata Gábor, Haïfa Zargayouna, Isabelle Tellier, Davide Buscaldi, Thierry Charnois. Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining. XVIth Symposium on Intelligent Data Analysis, Oct 2016, Stockholm, Sweden. pp.237 - 248, 2016, 〈10.1007/978-3-319-46349-0_21〉. 〈hal-01405041〉

Partager

Métriques

Consultations de la notice

152

Téléchargements de fichiers

381