Authors:
Asma Trabelsi
1
;
Séverine Soussilane
2
and
Emmanuel Helbert
2
Affiliations:
1
Alcatel-Lucent Enterprise, ALE International, 32, avenue Kléber 92700 Colombes, Paris, France
;
2
Master Data Science and Complex Systems, Université de Strasbourg, France
Keyword(s):
Voicemail Classification, Urgency Determination, BERT Embedding, Data Augmentation, Explainability.
Abstract:
Business field has improved exponentially during the last two decades: working methods have changed, more and more users are connected to each other across the globe, same teams as well as different teams can be separated by countries in big companies. So, users need a way to select messages to treat in priority for a better business management and a better communication. In this paper, we implement an approach enabling to classify voicemail messages into urgent and non urgent. The problem of determining urgency being still vast and open, some criteria should be used to decide the importance of messages depending to one’s necessity. Among these criteria, we can mention the sender position, the time of sending as well as the textual content. In this paper, we focus on classifying voicemail messages based on their contents. As there exist several Machine Learning approaches for text vectorization and classification, various combinations will be discussed and compared for the aim of fin
ding the most performant one.
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