1. Baroni, M., Dinu, G., Kruszewski, G.: Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), vol. 1, pp. 238–247. The Association for Computer Linguistics (2014)
2. Bellan, P., van der Aa, H., Dragoni, M., Ghidini, C., Ponzetto, S.P.: PET: an annotated dataset for process extraction from natural language text tasks. In: Cabanillas, C., Garmann-Johnsen, N.F., Koschmider, A. (eds.) Business Process Management Workshops (BPM 2022). LNBIP, vol. 460, pp. 315–321. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25383-6_23
3. Bellan, P., Dragoni, M., Ghidini, C.: A qualitative analysis of the state of the art in process extraction from text. In: Proceedings of the AIxIA 2020 Discussion Papers Workshop Co-located with the the 19th International Conference of the Italian Association for Artificial Intelligence (AIxIA2020), Anywhere, 27th November 2020. CEUR Workshop Proceedings, vol. 2776, pp. 19–30. CEUR-WS.org (2020)
4. Bellan, P., Dragoni, M., Ghidini, C.: Process extraction from text: state of the art and challenges for the future. arXiv preprint arXiv:2110.03754 (2021)
5. Boratko, M., Li, X., O’Gorman, T., Das, R., Le, D., McCallum, A.: ProtoQA: a question answering dataset for prototypical common-sense reasoning. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), pp. 1122–1136. ACL (2020)