A Closer Look to Your Business Network: Multitask Relation Extraction from Economic and Financial French Content - Université Toulouse 1 Capitole Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

A Closer Look to Your Business Network: Multitask Relation Extraction from Economic and Financial French Content

Résumé

Online textual content constitutes a valuable source of information for market stakeholders, enabling them to unveil their business network's most important operations and interactions , and to gain insights about their customers, business partners, and competitors, in order to make well-informed strategic decisions. Due to the problem of information overload , manually extracting this information remains a laborious task for professionals, making the use of Information Extraction technologies a powerful asset. In this context, this paper concerns discovering business relations between companies (e.g. company-partner) from French content on the web. We present a new dataset for business relation extraction at the sentence level and develop a set of deep learning experiments to distinguish between business vs. non-business relations , as well as identify five types of business relations according to a predefined taxonomy. Our results are encouraging , showing that multitask architectures are the most productive beating several strong state of the art baselines.
Fichier non déposé

Dates et versions

hal-03730345 , version 1 (20-07-2022)

Identifiants

  • HAL Id : hal-03730345 , version 1

Citer

Hadjer Khaldi, Farah Benamara, Camille Pradel, Nathalie Aussenac-Gilles. A Closer Look to Your Business Network: Multitask Relation Extraction from Economic and Financial French Content. Workshop on Knowledge Discovery from Unstructured Data in Financial Services (KDF @ AAAI 2022), AAAI: Association for the Advancement of Artificial Intelligence, Mar 2022, Vancouver, Canada. ⟨hal-03730345⟩
47 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More