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Categorization of B2B Service Offers: Lessons learnt from the Silex Use case

Molka Dhouib 1 Catherine Faron Zucker 2 Andrea Tettamanzi 2
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In the domain of Information Retrieval and Natural Language Processing, text classification has become a crucial task. In this article, we share our experience of text cate-gorization in an industrial context and we present a comparative evaluation of binary and multi-label classification algorithms applied to texts describing service offers, in the SILEX B2B platform. We show that for some use cases like the one we consider, a traditional representation of texts by "bags of words" gives better classification results than the promising representation by "word embeddings".
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Submitted on : Thursday, July 5, 2018 - 2:29:07 PM
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  • HAL Id : hal-01830905, version 1



Molka Dhouib, Catherine Faron Zucker, Andrea Tettamanzi. Categorization of B2B Service Offers: Lessons learnt from the Silex Use case. 4ème conférence sur les Applications Pratiques de l'Intelligence Artificielle APIA2018, Jul 2018, Nancy, France. ⟨hal-01830905⟩



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