Multimodal entity linking for tweets

Omar Adjali (1) , Romaric Besançon (1) , Olivier Ferret (1, 2) , Hervé Le Borgne (3, 1) , Brigitte Grau (4, 5)

1 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies
2 DIASI (CEA, LIST) - Département Intelligence Ambiante et Systèmes Interactifs
3 CEA - Commissariat à l'énergie atomique et aux énergies alternatives
4 ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise
5 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

In many information extraction applications, entity linking (EL) has emerged as a crucial task that allows leveraging information about named entities from a knowledge base. In this paper, we address the task of multimodal entity linking (MEL), an emerging research field in which textual and visual information is used to map an ambiguous mention to an entity in a knowledge base (KB). First, we propose a method for building a fully annotated Twitter dataset for MEL, where entities are defined in a Twitter KB. Then, we propose a model for jointly learning a representation of both mentions and entities from their textual and visual contexts. We demonstrate the effectiveness of the proposed model by evaluating it on the proposed dataset and highlight the importance of leveraging visual information when it is available.

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Omar Adjali, Romaric Besançon, Olivier Ferret, Hervé Le Borgne, Brigitte Grau. Multimodal entity linking for tweets. ECIR 2020 - 42nd European Conference on Information Retrieval Research, Apr 2020, Lisbonne (Online event), Portugal. pp.463-478, ⟨10.1007/978-3-030-45439-5⟩. ⟨hal-04315181⟩