  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.
