Extracting Relations in Texts with Concepts of Neighbours - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Extracting Relations in Texts with Concepts of Neighbours

Résumé

During the last decade, the need for reliable and massive Knowledge Graphs (KG) increased. KGs can be created in several ways: manually with forms or automatically with Information Extraction (IE), a natural language processing task for extracting knowledge from text. Relation Extraction is the part of IE that focuses on identifying relations between named entities in texts, which amounts to find new edges in a KG. Most recent approaches rely on deep learning, achieving state-ofthe-art performances. However, those performances are still too low to fully automatize the construction of reliable KGs, and human interaction remains necessary. This is made difficult by the statistical nature of deep learning methods that makes their predictions hardly interpretable. In this paper, we present a new symbolic and interpretable approach for Relation Extraction in texts. It is based on a modeling of the lexical and syntactic structure of text as a knowledge graph, and it exploits Concepts of Neighbours, a method based on Graph-FCA for computing similarities in knowledge graphs. An evaluation has been performed on a subset of TACRED (a relation extraction benchmark), showing promising results.
Fichier principal
Vignette du fichier
2021_ICFCA_Hugo.pdf (589.73 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03531335 , version 1 (18-01-2022)

Licence

Paternité

Identifiants

  • HAL Id : hal-03531335 , version 1

Citer

H. Ambre Ayats, Peggy Cellier, Sébastien Ferré. Extracting Relations in Texts with Concepts of Neighbours. International Conference In Formal Concepts Analysis, Jun 2021, Strasbourg, France. ⟨hal-03531335⟩
76 Consultations
73 Téléchargements

Partager

Gmail Facebook X LinkedIn More