Abstract
Mining useful information to analyze knowledge-intensive business processes requires data that describes activities of knowledge workers. Emails are widely used in organizations to provide support in the functioning of knowledge-intensive processes. The recent COVID-19 pandemic has increased reliance on technologies such as email to help facilitate communication within organizations to make up for the lack of face-to-face contact. In this work, we propose an activity mining technique, which receives an incoming email message, classifies the sender’s intent and translates it into a set of business process activities. Specifically, we leverage deep learning language models to first classify the email body into a group of intents, which are then mapped to related activities. To our knowledge, we propose the first transfer-learning based solution for mining activity information from emails. The effectiveness of our solution was evaluated on real-world data coming from email exchanges between knowledge workers. Our results based on unsupervised experiments and a field study show that transformer models can be used to semantically label emails and that mapping activities to matched intents is highly accurate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that a sentence can be represented as a bag of words or a sequence; our problem formulation is agnostic to how sentences are defined.
- 2.
For more information about these pre-trained models visit https://huggingface.co/.
References
Dustdar, S., Hoffmann, T., Van der Aalst, W.: Mining of ad-hoc business processes with teamlog. Data Knowl. Eng. 55(2), 129–158 (2005)
Corston-Oliver, S., Ringger, E., Gamon, M., Campbell, R.: Task-focused summarization of email. In: Text Summarization Branches Out, pp. 43–50 (2004)
Stuit, M., Wortmann, H.: Discovery and analysis of e-mail-driven business processes. Inf. Syst. 37(2), 142–168 (2012)
Bloom, N.: How working from home works out. Stanford Institute for Economic Policy Research, pp. 1–8 (2020)
Heavin, C., Power, D.J.: Challenges for digital transformation-towards a conceptual decision support guide for managers. J. Decis. Syst. 27(sup1), 38–45 (2018)
Wang, W., Hosseini, S., Awadallah, A.H., Bennett, P.N., Quirk, C.: Context-aware intent identification in email conversations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 585–594 (2019)
van der Aalst, W.M., Nikolov, A.: EmailAnalyzer: an e-mail mining plug-in for the prom framework. BPM Center Report BPM-07-16, BPMCenter.org (2007)
Lin, C.C., Kang, D., Gamon, M., Pantel, P.: Actionable email intent modeling with reparametrized RNNs. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Chambers, A.J., et al.: Automated business process discovery from unstructured natural-language documents. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds.) BPM 2020. LNBIP, vol. 397, pp. 232–243. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66498-5_18
Elleuch, M., Ismaili, O.A., Laga, N., Gaaloul, W., Benatallah, B.: Discovering activities from emails based on pattern discovery approach. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 88–104. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58638-6_6
Jlailaty, D., Grigori, D., Belhajjame, K.: On the elicitation and annotation of business activities based on emails. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 101–103 (2019)
Alibadi, Z., Du, M., Vidal, J.M.: Using pre-trained embeddings to detect the intent of an email. In: Proceedings of the 7th ACIS International Conference on Applied Computing and Information Technology, pp. 1–7 (2019)
Radford, A., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)
Brown, T.B., et al.: Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020)
Romera-Paredes, B., Torr, P.: An embarrassingly simple approach to zero-shot learning. In: International Conference on Machine Learning, pp. 2152–2161. PMLR (2015)
Cohen, W., Carvalho, V., Mitchell, T.: Learning to classify email into “speech acts”. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 309–316 (2004)
Searle, J.R., Searle, J.R.: Speech Acts: An Essay in the Philosophy of Language, vol. 626. Cambridge University Press (1969)
Carvalho, V.R., Cohen, W.W.: Learning to extract signature and reply lines from email. In: Proceedings of the Conference on Email and Anti-Spam, vol. 2004 (2004)
El Emam, K., Madhavji, N.H.: A field study of requirements engineering practices in information systems development. In: Proceedings of 1995 IEEE International Symposium on Requirements Engineering (RE 1995), pp. 68–80. IEEE (1995)
Wang, X., Xu, Y.: An improved index for clustering validation based on silhouette index and Calinski-Harabasz index. In: IOP Conference Series: Materials Science and Engineering, vol. 569, p. 052024. IOP Publishing (2019)
Yin, W., Hay, J., Roth, D.: Benchmarking zeroshot text classification: datasets, evaluation and entailment approach. arXiv preprint arXiv:1909.00161 (2019)
Sappadla, P.V., Nam, J., Mencía, E.L., Fürnkranz, J.: Using semantic similarity for multi-label zero-shot classification of text documents. In: ESANN (2016)
Di Ciccio, C., Mecella, M.: Mining artful processes from knowledge workers’ emails. IEEE Internet Comput. 17(5), 10–20 (2013)
Shu, K., Mukherjee, S., Zheng, G., Awadallah, A.H., Shokouhi, M., Dumais, S.: Learning with weak supervision for email intent detection. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1051–1060 (2020)
Pearl, J., Mackenzie, D.: The Book of Why: The New Science of Cause and Effect. Basic Books (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
Cite this paper
Khandaker, F., Senderovich, A., Yu, E., Carbajales, S., Chan, A. (2023). Transformer Models for Activity Mining in Knowledge-Intensive Processes. In: Cabanillas, C., Garmann-Johnsen, N.F., Koschmider, A. (eds) Business Process Management Workshops. BPM 2022. Lecture Notes in Business Information Processing, vol 460. Springer, Cham. https://doi.org/10.1007/978-3-031-25383-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-25383-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25382-9
Online ISBN: 978-3-031-25383-6
eBook Packages: Computer ScienceComputer Science (R0)