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Reducing Event Variability in Logs by Clustering of Word Embeddings

  • David Sánchez-CharlesEmail author
  • Josep Carmona
  • Victor Muntés-Mulero
  • Marc Solé
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)

Abstract

Several business-to-business and business-to-consumer services are provided as a human-to-human conversation in which the provider representative guides the conversation towards its resolution based on her experience, following internal guidelines. Several attempts to automatize these services are becoming popular, but they are currently limited to procedures and objectives set during design step. Process discovery techniques could provide the necessary mechanisms to monitor event logs derived from textual conversations and expand the capabilities of conversational bots. Still, variability of textual messages hinders the utility of process discovery techniques by producing non-understandable unstructured process models. In this paper, we propose the usage of word embedding for combining events that have a semantically similar name.

Keywords

Unstructured processes Process discovery Machine learning Word embedding 

Notes

Acknowledgements

This work is funded by Secretaria de Universitats i Recerca of Generalitat de Catalunya, under the Industrial Doctorate Program 2013DI062, and the Spanish Ministry for Economy and Competitiveness, the European Union (FEDER funds) under grant COMMAS (Ref. TIN2013-46181-C2-1-R).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • David Sánchez-Charles
    • 1
    Email author
  • Josep Carmona
    • 2
  • Victor Muntés-Mulero
    • 1
  • Marc Solé
    • 1
  1. 1.CA Strategic Research, CA TechnologiesBarcelonaSpain
  2. 2.Universitat Politècnica de CatalunyaBarcelonaSpain

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