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.
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Notes
- 1.
For instance, a faster customer support channel leads to lower customer churn rates. https://www.salesforce.com/blog/2017/03/effective-strategies-to-reduce-customer-churn.html.
- 2.
For the sake of simplicity, the definitions and examples of the paper are tailored to the context of conversations between humans and, possibly, computers. In spite of this, the theory of the paper can be applied to general event logs as defined in [18].
- 3.
We follow the classical definition \(idf(w) = \log \frac{\text {Number of documents}}{\text {Occurrences of } w}\).
- 4.
During the evaluation of this approach, we set c to 1.2 and b to 0.75 as proposed by [11].
- 5.
i.e. a finite collection of sets \(\{ E_i \}_{i \in I}\) such that \(\cup _{i \in I} E_i = E\) and \(E_i \cap E_j = \emptyset \) for any \(i \not = j\).
- 6.
- 7.
8th August 2016. The dataset is publicly available on data.4tu.nl [16].
- 8.
- 9.
The flower model is a model that allows any possible behavior.
- 10.
We run the infrequent version of the Inductive Miner, with default parameters, on ProM 6.5.1.
- 11.
Results are consistent with respect to a \(20\%\)-out cross-validation.
- 12.
ISO 224617-2 defines 57 generic communicative functions, that one may enrich or refine depending with domain knowledge.
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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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Sánchez-Charles, D., Carmona, J., Muntés-Mulero, V., Solé, M. (2018). Reducing Event Variability in Logs by Clustering of Word Embeddings. In: Teniente, E., Weidlich, M. (eds) Business Process Management Workshops. BPM 2017. Lecture Notes in Business Information Processing, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-319-74030-0_14
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