{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:42:04Z","timestamp":1782985324176,"version":"3.54.5"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892980","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":1,"title":["Beyond Transformers: fault type detection in maintenance tickets with Kernel Methods, Boost Decision Trees and Neural Networks"],"prefix":"10.1109","author":[{"given":"Stefano","family":"Campese","sequence":"first","affiliation":[{"name":"University of Padua,Department of Physics and Astronomy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Agostini","sequence":"additional","affiliation":[{"name":"University of Padua,Department of Physics and Astronomy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacopo","family":"Pazzini","sequence":"additional","affiliation":[{"name":"University of Padua,Department of Physics and Astronomy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Pozza","sequence":"additional","affiliation":[{"name":"Omnys SRL,R&D Department"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Mklpy: a python-based framework for multiple kernel learning","author":"lauriola","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref11","article-title":"Random features for large-scale kernel machines","volume":"20","author":"rahimi","year":"2007","journal-title":"Advances in neural information processing systems"},{"key":"ref12","article-title":"On the nyström method for approximating a gram matrix for improved kernel-based learning","volume":"6","author":"drineas","year":"2005","journal-title":"Journal of Machine Learning Research"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref14","author":"vaswani","year":"2017","journal-title":"Attention is all you need"},{"key":"ref15","article-title":"Roberta: A robustly optimized bert pretraining approach","author":"liu","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref16","article-title":"Electra: Pre-training text encoders as discriminators rather than generators","author":"clark","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref17","author":"schweter","year":"2020","journal-title":"Italian bert and electra models"},{"key":"ref18","author":"parisi","year":"0","journal-title":"Umberto an italian language model trained with whole word masking"},{"key":"ref19","author":"ravasio","year":"0","journal-title":"Gilberto An italian pretrained language model based on roberta"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3412841.3442109"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103150"},{"key":"ref6","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref5","author":"nokudaiyaval","year":"0","journal-title":"Automatic tagging of customer support tickets using bert algorithm"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809682"},{"key":"ref7","article-title":"Clustering support tickets with natural language processing: K-means applied to transformer embeddings","author":"compton","year":"2020","journal-title":"Tech Rep"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3032840"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219851"},{"key":"ref9","first-page":"1","article-title":"Support vector machines-kernels and the kernel trick","volume":"26","author":"hofmann","year":"2006","journal-title":"notes"},{"key":"ref20","first-page":"1","article-title":"Alberto: Italian bert language understanding model for nlp challenging tasks based on tweets","volume":"2481","author":"polignano","year":"2019","journal-title":"6th Italian Conference on Computational Linguistics CLiC-it 2019"},{"key":"ref22","first-page":"48","article-title":"Psy-chiatric disorders classification with 3d convolutional neural networks","author":"campese","year":"2019","journal-title":"In INNS Big Data and Deep Learning conference"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107194"},{"key":"ref23","article-title":"Tune: A research platform for distributed model selection and training","author":"liaw","year":"2018","journal-title":"ArXiv Preprint"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","location":"Padua, Italy","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892980.pdf?arnumber=9892980","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T22:57:21Z","timestamp":1667516241000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892980\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892980","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}