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

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-03730345
Contributor : Farah Benamara Connect in order to contact the contributor
Submitted on : Wednesday, July 20, 2022 - 5:22:27 PM
Last modification on : Wednesday, August 24, 2022 - 3:48:00 PM

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  • HAL Id : hal-03730345, version 1

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Hadjer Khaldi, Farah Benamara, Camille Pradel, Nathalie Aussenac. 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⟩

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