{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T12:11:20Z","timestamp":1762863080044,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030594299"},{"type":"electronic","value":"9783030594305"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59430-5_5","type":"book-chapter","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T08:04:59Z","timestamp":1601021099000},"page":"58-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["BERT-Based Sentiment Analysis Using Distillation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3889-8069","authenticated-orcid":false,"given":"Jan","family":"Lehe\u010dka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8362-5927","authenticated-orcid":false,"given":"Jan","family":"\u0160vec","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6967-1687","authenticated-orcid":false,"given":"Pavel","family":"Ircing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8169-2410","authenticated-orcid":false,"given":"Lubo\u0161","family":"\u0160m\u00eddl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,26]]},"reference":[{"key":"5_CR1","unstructured":"Brychc\u00edn, T., Habernal, I.: Unsupervised improving of sentiment analysis using global target context. In: Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013, pp. 122\u2013128 (2013)"},{"key":"5_CR2","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"5_CR3","unstructured":"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)"},{"key":"5_CR4","unstructured":"Habernal, I., Pt\u00e1\u010dek, T., Steinberger, J.: Sentiment analysis in Czech social media using supervised machine learning. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 65\u201374 (2013)"},{"issue":"3","key":"5_CR5","doi-asserted-by":"publisher","first-page":"365","DOI":"10.13053\/cys-20-3-2469","volume":"20","author":"T Hercig","year":"2016","unstructured":"Hercig, T., Brychc\u00edn, T., Svoboda, L., Konkol, M., Steinberger, J.: Unsupervised methods to improve aspect-based sentiment analysis in Czech. Computaci\u00f3n y Sistemas 20(3), 365\u2013375 (2016)","journal-title":"Computaci\u00f3n y Sistemas"},{"key":"5_CR6","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"5_CR7","unstructured":"Hoang, M., Bihorac, O.A., Rouces, J.: Aspect-based sentiment analysis using BERT. In: Proceedings of the 22nd Nordic Conference on Computational Linguistics, pp. 187\u2013196. Link\u00f6ping University Electronic Press, Turku, September\u2013October 2019. https:\/\/www.aclweb.org\/anthology\/W19-6120"},{"key":"5_CR8","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. arXiv preprint arXiv:1902.00751 (2019)"},{"issue":"4","key":"5_CR9","first-page":"330","volume":"30","author":"DMEDM Hussein","year":"2018","unstructured":"Hussein, D.M.E.D.M.: A survey on sentiment analysis challenges. J. King Saud Univ. Eng. Sci. 30(4), 330\u2013338 (2018)","journal-title":"J. King Saud Univ. Eng. Sci."},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Jiao, X., et al.: TinyBERT: distilling BERT for natural language understanding. arXiv preprint arXiv:1909.10351 (2019)","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.csl.2019.01.001","volume":"56","author":"T Kincl","year":"2019","unstructured":"Kincl, T., Nov\u00e1k, M., P\u0159ibil, J.: Improving sentiment analysis performance on morphologically rich languages: language and domain independent approach. Comput. Speech Lang. 56, 36\u201351 (2019)","journal-title":"Comput. Speech Lang."},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Kudo, T., Richardson, J.: SentencePiece: a simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018)","DOI":"10.18653\/v1\/D18-2012"},{"key":"5_CR13","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/978-3-030-58323-1_23","volume-title":"Text, Speech, and Dialogue","author":"J Lehe\u010dka","year":"2020","unstructured":"Lehe\u010dka, J., \u0160vec, J., Ircing, P., \u0160m\u00eddl, L.: Adjusting BERT\u2019s pooling layer for large-scale multi-label text classification. In: Sojka, P., Kope\u010dek, I., Pala, K., Hor\u00e1k, A. (eds.) TSD 2020. LNCS (LNAI), vol. 12284, pp. 214\u2013221. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58323-1_23"},{"key":"5_CR14","unstructured":"Lenc, L., Hercig, T.: Neural networks for sentiment analysis in Czech. In: ITAT, pp. 48\u201355 (2016)"},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"46868","DOI":"10.1109\/ACCESS.2020.2978511","volume":"8","author":"X Li","year":"2020","unstructured":"Li, X., et al.: Enhancing BERT representation with context-aware embedding for aspect-based sentiment analysis. IEEE Access 8, 46868\u201346876 (2020)","journal-title":"IEEE Access"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhou, P., Zhao, Z., Wang, Z., Deng, H., Ju, Q.: FastBERT: a self-distilling BERT with adaptive inference time. arXiv preprint arXiv:2004.02178 (2020)","DOI":"10.18653\/v1\/2020.acl-main.537"},{"key":"5_CR17","unstructured":"Ma, X., Xu, P., Wang, Z., Nallapati, R., Xiang, B.: Universal text representation from BERT: an empirical study. arXiv preprint arXiv:1910.07973 (2019)"},{"key":"5_CR18","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cviu.2017.05.007","volume":"161","author":"D Mishkin","year":"2017","unstructured":"Mishkin, D., Sergievskiy, N., Matas, J.: Systematic evaluation of convolution neural network advances on the Imagenet. Comput. Vis. Image Underst. 161, 11\u201319 (2017). https:\/\/doi.org\/10.1016\/j.cviu.2017.05.007. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1077314217300814","journal-title":"Comput. Vis. Image Underst."},{"key":"5_CR19","unstructured":"Rietzler, A., Stabinger, S., Opitz, P., Engl, S.: Adapt or get left behind: domain adaptation through BERT language model finetuning for aspect-target sentiment classification. arXiv preprint arXiv:1908.11860 (2019)"},{"key":"5_CR20","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-030-44289-7_6","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)","author":"SA Salloum","year":"2020","unstructured":"Salloum, S.A., Khan, R., Shaalan, K.: A survey of semantic analysis approaches. In: Hassanien, A.-E., Azar, A.T., Gaber, T., Oliva, D., Tolba, F.M. (eds.) AICV 2020. AISC, vol. 1153, pp. 61\u201370. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-44289-7_6"},{"key":"5_CR21","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)"},{"key":"5_CR22","unstructured":"Song, Y., Wang, J., Liang, Z., Liu, Z., Jiang, T.: Utilizing BERT intermediate layers for aspect based sentiment analysis and natural language inference. arXiv preprint arXiv:2002.04815 (2020)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Steinberger, J., Brychc\u00edn, T., Konkol, M.: Aspect-level sentiment analysis in Czech. In: Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 24\u201330 (2014)","DOI":"10.3115\/v1\/W14-2605"},{"issue":"4","key":"5_CR24","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.dss.2012.05.029","volume":"53","author":"J Steinberger","year":"2012","unstructured":"Steinberger, J., et al.: Creating sentiment dictionaries via triangulation. Decis. Support Syst. 53(4), 689\u2013694 (2012)","journal-title":"Decis. Support Syst."},{"key":"5_CR25","unstructured":"Sun, C., Huang, L., Qiu, X.: Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv preprint arXiv:1903.09588 (2019)"},{"issue":"2","key":"5_CR26","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s10579-013-9246-z","volume":"48","author":"J \u0160vec","year":"2013","unstructured":"\u0160vec, J., et al.: General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes. Lang. Resour. Eval. 48(2), 227\u2013248 (2013). https:\/\/doi.org\/10.1007\/s10579-013-9246-z","journal-title":"Lang. Resour. Eval."},{"key":"5_CR27","unstructured":"Tamchyna, A., Fiala, O., Veselovsk\u00e1, K.: Czech aspect-based sentiment analysis: a new dataset and preliminary results. In: ITAT, pp. 95\u201399 (2015)"},{"key":"5_CR28","unstructured":"Tang, R., Lu, Y., Liu, L., Mou, L., Vechtomova, O., Lin, J.: Distilling task-specific knowledge from BERT into simple neural networks. arXiv preprint arXiv:1903.12136 (2019)"},{"key":"5_CR29","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Veselovsk\u00e1, K.: Sentence-level sentiment analysis in Czech. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, pp. 1\u20134 (2012)","DOI":"10.1145\/2254129.2254208"},{"key":"5_CR31","unstructured":"Veselovsk\u00e1, K.: Sentiment analysis in Czech. Studies in Computational and Theoretical Linguistics, vol. 16. \u00daFAL, Praha, Czechia (2017)"},{"key":"5_CR32","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/978-3-030-58323-1_35","volume-title":"Text, Speech, and Dialogue","author":"J \u0160vec","year":"2020","unstructured":"\u0160vec, J., Lehe\u010dka, J., \u0160m\u00eddl, L., Ircing, P.: Automatic correction of i\/y spelling in Czech ASR output. In: Sojka, P., Kope\u010dek, I., Pala, K., Hor\u00e1k, A. (eds.) TSD 2020. LNCS (LNAI), vol. 12284, pp. 321\u2013330. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58323-1_35"}],"container-title":["Lecture Notes in Computer Science","Statistical Language and Speech Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59430-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T10:54:12Z","timestamp":1619261652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59430-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030594299","9783030594305"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59430-5_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"26 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SLSP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Statistical Language and Speech Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cardiff","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"slsp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/irdta.eu\/slsp2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"52% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID-19 pandemic the conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}