{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T16:39:28Z","timestamp":1742402368002,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["Funded Project CYBELE under grant agreement no 825355"],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10994-022-06151-6","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T21:04:27Z","timestamp":1648674267000},"page":"1287-1313","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety"],"prefix":"10.1007","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6165-7239","authenticated-orcid":false,"given":"Georgios","family":"Makridis","sequence":"first","affiliation":[]},{"given":"Philip","family":"Mavrepis","sequence":"additional","affiliation":[]},{"given":"Dimosthenis","family":"Kyriazis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"6151_CR1","unstructured":"Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D.C., Rangapuram, S., Salinas, D., & Schulz, J., et\u00a0al. (2019) Gluonts: Probabilistic time series models in python. arXiv:1906.05264"},{"key":"6151_CR2","unstructured":"Batista, D.S. (2018). Named-entity evaluation metrics based on entity-level. https:\/\/www.davidsbatista.net\/blog\/2018\/05\/09\/Named_Entity_Evaluation\/."},{"key":"6151_CR3","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., & Cohan, A. (2019). Scibert: A pretrained language model for scientific text. arXiv:1903.10676","DOI":"10.18653\/v1\/D19-1371"},{"key":"6151_CR4","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Advances in neural information processing systems (pp. 153\u2013160).","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"6151_CR5","doi-asserted-by":"crossref","unstructured":"Calabuig, J., Falciani, H., & S\u00e1nchez-P\u00e9rez, E. (2020) Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets. Neurocomputing","DOI":"10.1016\/j.neucom.2020.02.052"},{"issue":"3","key":"6151_CR6","first-page":"277","volume":"13","author":"YW Cheung","year":"1995","unstructured":"Cheung, Y. W., & Lai, K. S. (1995). Lag order and critical values of the augmented dickey-fuller test. Journal of Business & Economic Statistics, 13(3), 277\u2013280.","journal-title":"Journal of Business & Economic Statistics"},{"issue":"ARTICLE","key":"6151_CR7","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(ARTICLE), 2493\u20132537.","journal-title":"Journal of Machine Learning Research"},{"key":"6151_CR8","unstructured":"Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"issue":"4","key":"6151_CR9","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.046128","volume":"64","author":"KT Dolan","year":"2001","unstructured":"Dolan, K. T., & Spano, M. L. (2001). Surrogate for nonlinear time series analysis. Physical Review E, 64(4), 046128.","journal-title":"Physical Review E"},{"key":"6151_CR10","doi-asserted-by":"crossref","unstructured":"Fong, S.J., Li, G., Dey, N., Crespo, R.G., & Herrera-Viedma, E. (2020) Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak. arXiv:2003.10776","DOI":"10.9781\/ijimai.2020.02.002"},{"issue":"1","key":"6151_CR11","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","volume":"24","author":"TC Fu","year":"2011","unstructured":"Fu, T. C. (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164\u2013181.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"11","key":"6151_CR12","doi-asserted-by":"publisher","first-page":"1933","DOI":"10.4315\/0362-028X.JFP-13-171","volume":"76","author":"SM Gendel","year":"2013","unstructured":"Gendel, S. M., & Zhu, J. (2013). Analysis of us food and drug administration food allergen recalls after implementation of the food allergen labeling and consumer protection act. Journal of Food Protection, 76(11), 1933\u20131938.","journal-title":"Journal of Food Protection"},{"key":"6151_CR13","doi-asserted-by":"crossref","unstructured":"Hammerton, J. (2003). Named entity recognition with long short-term memory. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 (pp. 172\u2013175)","DOI":"10.3115\/1119176.1119202"},{"key":"6151_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"key":"6151_CR15","doi-asserted-by":"crossref","unstructured":"Hessel, M., Modayil, J., Van\u00a0Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., & Silver, D. (2018). Rainbow: Combining improvements in deep reinforcement learning. In Thirty-Second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11796"},{"key":"6151_CR16","unstructured":"Honnibal, M., & Montani, I. (2017). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear"},{"key":"6151_CR17","unstructured":"https:\/\/www.agroknow.com\/"},{"key":"6151_CR18","doi-asserted-by":"publisher","unstructured":"Karamanolakis, G., Ma, J., & Dong, X.L. (2020). TXtract: Taxonomy-aware knowledge extraction for thousands of product categories. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8489\u20138502. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.751. https:\/\/aclanthology.org\/2020.acl-main.751","DOI":"10.18653\/v1\/2020.acl-main.751"},{"key":"6151_CR19","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.C., Yang, Y., & Liu, H. (2018). Modeling long-and short-term temporal patterns with deep neural networks. In The 41st international ACM SIGIR conference on research & development in information retrieval (pp. 95\u2013104).","DOI":"10.1145\/3209978.3210006"},{"key":"6151_CR20","doi-asserted-by":"crossref","unstructured":"Lebret, R., & Collobert, R. (2013) Word emdeddings through hellinger pca. arXiv:1312.5542","DOI":"10.3115\/v1\/E14-1051"},{"key":"6151_CR21","unstructured":"Li, J., Sun, A., Han, J., & Li, C. (2020). A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering"},{"key":"6151_CR22","doi-asserted-by":"crossref","unstructured":"Luo, F., Fang, P., Qiu, Q., & Xiao, H. (2021). Features induction for product named entity recognition with CRFS. In Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 491\u2013496). IEEE","DOI":"10.1109\/CSCWD.2012.6221863"},{"issue":"3","key":"6151_CR23","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1093\/jamiaopen\/ooz030","volume":"2","author":"A Maharana","year":"2019","unstructured":"Maharana, A., Cai, K., Hellerstein, J., Hswen, Y., Munsell, M., Staneva, V., et al. (2019). Detecting reports of unsafe foods in consumer product reviews. JAMIA Open, 2(3), 330\u2013338.","journal-title":"JAMIA Open"},{"key":"6151_CR24","doi-asserted-by":"crossref","unstructured":"Makridis, G., Mavrepis, P., Kyriazis, D., Polychronou, I., & Kaloudis, S. (2020). Enhanced food safety through deep learning for food recalls prediction. In International conference on discovery science, Springer (pp. 566\u2013580).","DOI":"10.1007\/978-3-030-61527-7_37"},{"issue":"11","key":"6151_CR25","doi-asserted-by":"publisher","first-page":"2286","DOI":"10.1080\/10408398.2016.1257481","volume":"57","author":"HJ Marvin","year":"2017","unstructured":"Marvin, H. J., Janssen, E. M., Bouzembrak, Y., Hendriksen, P. J., & Staats, M. (2017). Big data in food safety: An overview. Critical Reviews in Food Science and Nutrition, 57(11), 2286\u20132295.","journal-title":"Critical Reviews in Food Science and Nutrition"},{"key":"6151_CR26","unstructured":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv:1301.3781"},{"key":"6151_CR27","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111\u20133119)."},{"key":"6151_CR28","doi-asserted-by":"crossref","unstructured":"Nalmpantis, C., & Vrakas, D. (2019). Signal2vec: Time series embedding representation. In International conference on engineering applications of neural networks, Springer (pp. 80\u201390).","DOI":"10.1007\/978-3-030-20257-6_7"},{"issue":"5","key":"6151_CR29","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1111\/j.1753-4887.2010.00286.x","volume":"68","author":"DG Nyachuba","year":"2010","unstructured":"Nyachuba, D. G. (2010). Foodborne illness: Is it on the rise? Nutrition Reviews, 68(5), 257\u2013269.","journal-title":"Nutrition Reviews"},{"key":"6151_CR30","unstructured":"Oord, A.v.d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio. arXiv:1609.03499"},{"key":"6151_CR31","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.ins.2019.01.076","volume":"484","author":"ARS Parmezan","year":"2019","unstructured":"Parmezan, A. R. S., Souza, V. M., & Batista, G. E. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, 484, 302\u2013337.","journal-title":"Information Sciences"},{"key":"6151_CR32","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830.","journal-title":"Journal of Machine Learning Research"},{"key":"6151_CR33","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., & Manning, C.D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532\u20131543).","DOI":"10.3115\/v1\/D14-1162"},{"key":"6151_CR34","unstructured":"Pfeiffer, J., Simpson, E., & Gurevych, I. (2020). Low resource multi-task sequence tagging\u2013revisiting dynamic conditional random fields. arXiv:2005.00250."},{"key":"6151_CR35","doi-asserted-by":"crossref","unstructured":"Popovski, G., Kochev, S., Seljak, B.K., & Eftimov, T. (2019). Foodie: a rule-based named-entity recognition method for food information extraction. In Proceedings of the 8th international conference on pattern recognition applications and methods (pp. 915\u2013922).","DOI":"10.5220\/0007686309150922"},{"key":"6151_CR36","doi-asserted-by":"publisher","first-page":"31586","DOI":"10.1109\/ACCESS.2020.2973502","volume":"8","author":"G Popovski","year":"2020","unstructured":"Popovski, G., Seljak, B. K., & Eftimov, T. (2020). A survey of named-entity recognition methods for food information extraction. IEEE Access, 8, 31586\u201331594.","journal-title":"IEEE Access"},{"key":"6151_CR37","doi-asserted-by":"crossref","unstructured":"Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2019). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"6151_CR38","unstructured":"Segura\u00a0Bedmar, I., Martinez, P., & Herrero\u00a0Zazo, M. (2013). Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013). Association for Computational Linguistics"},{"key":"6151_CR39","doi-asserted-by":"crossref","unstructured":"Sherstov, A.A., & Stone, P. (2005). Function approximation via tile coding: Automating parameter choice. In International symposium on abstraction, reformulation, and approximation, Springer (pp. 194\u2013205).","DOI":"10.1007\/11527862_14"},{"key":"6151_CR40","unstructured":"Socher, R., Huval, B., Manning, C.D., & Ng, A.Y. (2012). Semantic compositionality through recursive matrix-vector spaces. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, (pp. 1201\u20131211)."},{"key":"6151_CR41","unstructured":"Socher, R., Manning, C.D., & Ng, A.Y. (2010). Learning continuous phrase representations and syntactic parsing with recursive neural networks. In Proceedings of the NIPS-2010 deep learning and unsupervised feature learning workshop, vol. 2010, pp. 1\u20139"},{"key":"6151_CR42","doi-asserted-by":"crossref","unstructured":"Strubell, E., Verga, P., Belanger, D., & McCallum, A. (2017). Fast and accurate entity recognition with iterated dilated convolutions. arXiv:1702.02098.","DOI":"10.18653\/v1\/D17-1283"},{"key":"6151_CR43","doi-asserted-by":"crossref","unstructured":"Sundheim, B.M., & Chinchor, N. (1993). Survey of the message understanding conferences. In HUMAN LANGUAGE TECHNOLOGY: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993","DOI":"10.3115\/1075671.1075684"},{"key":"6151_CR44","volume-title":"Introduction to reinforcement learning","author":"RS Sutton","year":"1998","unstructured":"Sutton, R. S., Barto, A. G., et al. (1998). Introduction to reinforcement learning (Vol. 135). Cambridge: MIT Press."},{"key":"6151_CR45","doi-asserted-by":"crossref","unstructured":"Tsuboi, Y. (2014). Neural networks leverage corpus-wide information for part-of-speech tagging. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), (pp. 938\u2013950).","DOI":"10.3115\/v1\/D14-1101"},{"key":"6151_CR46","unstructured":"Wang, Y., Smola, A., Maddix, D.C., Gasthaus, J., Foster, D., & Januschowski, T. (2019). Deep factors for forecasting. arXiv:1905.12417"},{"key":"6151_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2017.2732945","volume":"5","author":"E Yom-Tov","year":"2017","unstructured":"Yom-Tov, E. (2017). Predicting drug recalls from internet search engine queries. IEEE journal of Translational Engineering in Health and Medicine, 5, 1\u20136.","journal-title":"IEEE journal of Translational Engineering in Health and Medicine"},{"issue":"2","key":"6151_CR48","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3905\/jfds.2020.1.030","volume":"2","author":"Z Zhang","year":"2020","unstructured":"Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep reinforcement learning for trading. The Journal of Financial Data Science, 2(2), 25\u201340.","journal-title":"The Journal of Financial Data Science"},{"issue":"2","key":"6151_CR49","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s10579-008-9066-8","volume":"42","author":"J Zhao","year":"2008","unstructured":"Zhao, J., & Liu, F. (2008). Product named entity recognition in Chinese text. Language Resources and Evaluation, 42(2), 197\u2013217.","journal-title":"Language Resources and Evaluation"},{"key":"6151_CR50","doi-asserted-by":"crossref","unstructured":"Zheng, G., Mukherjee, S., Dong, X.L., & Li, F. (2018). Opentag: Open attribute value extraction from product profiles. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, (pp. 1049\u20131058).","DOI":"10.1145\/3219819.3219839"},{"key":"6151_CR51","doi-asserted-by":"crossref","unstructured":"Zhong, Z., & Chen, D. (2020). A frustratingly easy approach for entity and relation extraction. arXiv:2010.12812.","DOI":"10.18653\/v1\/2021.naacl-main.5"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06151-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06151-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06151-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T12:36:46Z","timestamp":1700397406000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06151-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,30]]},"references-count":51,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["6151"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06151-6","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2022,3,30]]},"assertion":[{"value":"28 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"Not Applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not Applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not Applicable","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"A Git repository containing all the code for this project is created that can be found at this link Github Repository.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}