{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:10:23Z","timestamp":1743153023600,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985395"},{"type":"electronic","value":"9789819985401"}],"license":[{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8540-1_9","type":"book-chapter","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T18:01:32Z","timestamp":1703440892000},"page":"101-112","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Pre-trained Model for\u00a0Chinese Medical Record Punctuation Restoration"],"prefix":"10.1007","author":[{"given":"Zhipeng","family":"Yu","sequence":"first","affiliation":[]},{"given":"Tongtao","family":"Ling","sequence":"additional","affiliation":[]},{"given":"Fangqing","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Huangxu","family":"Sheng","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,25]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Che, W., Feng, Y., Qin, L., Liu, T.: N-ltp: an open-source neural language technology platform for chinese. arXiv preprint arXiv:2009.11616 (2020)","DOI":"10.18653\/v1\/2021.emnlp-demo.6"},{"key":"9_CR2","unstructured":"Che, X., Wang, C., Yang, H., Meinel, C.: Punctuation prediction for unsegmented transcript based on word vector. In: Language Resources and Evaluation (2016)"},{"key":"9_CR3","unstructured":"Che, X., Wang, C., Yang, H., Meinel, C.: Punctuation prediction for unsegmented transcript based on word vector. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 654\u2013658 (2016)"},{"key":"9_CR4","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Cui, Y., et al.: Pre-training with whole word masking for chinese bert. In: Institute of Electrical and Electronics Engineers (IEEE) (2021)","DOI":"10.1109\/TASLP.2021.3124365"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 4171\u20134186 (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Graves, A., Graves, A.: Long short-term memory. In: Supervised Sequence Labelling with Recurrent Neural Networks, pp. 37\u201345 (2012)","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"9_CR8","doi-asserted-by":"publisher","unstructured":"Hentschel, M., Tsunoo, E., Okuda, T.: Making punctuation restoration robust and fast with multi-task learning and knowledge distillation. In: 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021), pp. 7773\u20137777 (2021). https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414518","DOI":"10.1109\/ICASSP39728.2021.9414518"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Kalogriopoulos, N.A., Baran, J., Nimunkar, A.J., Webster, J.G.: Electronic medical record systems for developing countries. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1730\u20131733. IEEE (2009)","DOI":"10.1109\/IEMBS.2009.5333561"},{"key":"9_CR10","unstructured":"Khosla, P., et al.: Supervised contrastive learning. Adv. Neural Inf. Process. Syst. 33, 18661\u201318673 (2020)"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Klejch, O., Bell, P., Renals, S.: Sequence-to-sequence models for punctuated transcription combining lexical and acoustic features. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) (2017)","DOI":"10.1109\/ICASSP.2017.7953248"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Ling, T., Chen, L., Lai, Y., Liu, H.L.: Evolutionary verbalizer search for prompt-based few shot text classification. arXiv preprint arXiv:2306.10514 (2023)","DOI":"10.1007\/978-3-031-40292-0_23"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Ling, T., Chen, L., Sheng, H., Cai, Z., Liu, H.L.: Sentence-level event detection without triggers via prompt learning and machine reading comprehension. arXiv preprint arXiv:2306.14176 (2023)","DOI":"10.1007\/978-3-031-46674-8_3"},{"key":"9_CR15","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"9_CR16","doi-asserted-by":"publisher","unstructured":"Makhija, K., Ho, T.N., Chng, E.S.: Transfer learning for punctuation prediction. In: 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 268\u2013273 (2019). https:\/\/doi.org\/10.1109\/APSIPAASC47483.2019.9023200","DOI":"10.1109\/APSIPAASC47483.2019.9023200"},{"key":"9_CR17","doi-asserted-by":"publisher","first-page":"9411","DOI":"10.1007\/s11042-020-10073-7","volume":"80","author":"M Malik","year":"2021","unstructured":"Malik, M., Malik, M.K., Mehmood, K., Makhdoom, I.: Automatic speech recognition: a survey. Multim. Tools Appl. 80, 9411\u20139457 (2021)","journal-title":"Multim. Tools Appl."},{"key":"9_CR18","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"issue":"10","key":"9_CR19","doi-asserted-by":"publisher","first-page":"2965","DOI":"10.1016\/j.patcog.2008.05.008","volume":"41","author":"D O\u2019Shaughnessy","year":"2008","unstructured":"O\u2019Shaughnessy, D.: Automatic speech recognition: history, methods and challenges. Pattern Recogn. 41(10), 2965\u20132979 (2008)","journal-title":"Pattern Recogn."},{"key":"9_CR20","unstructured":"Peitz, S., Freitag, M., Mauser, A., Ney, H.: Modeling punctuation prediction as machine translation. In: Proceedings of the 8th International Workshop on Spoken Language Translation: Papers, pp. 238\u2013245 (2011)"},{"key":"9_CR21","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)"},{"key":"9_CR22","doi-asserted-by":"publisher","unstructured":"Salloum, W., Finley, G., Edwards, E., Miller, M., Suendermann-Oeft, D.: Deep learning for punctuation restoration in medical reports. In: BioNLP 2017, pp. 159\u2013164 (2017). https:\/\/doi.org\/10.18653\/v1\/W17-2319","DOI":"10.18653\/v1\/W17-2319"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Xu, K., Xie, L., Yao, K.: Investigating LSTM for punctuation prediction. In: 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), pp. 1\u20135. IEEE (2016)","DOI":"10.1109\/ISCSLP.2016.7918492"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Yi, J., Tao, J.: Self-attention based model for punctuation prediction using word and speech embeddings. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) (2019)","DOI":"10.1109\/ICASSP.2019.8682260"},{"key":"9_CR25","unstructured":"Yi, J., Tao, J., Bai, Y., Tian, Z., Fan, C.: Adversarial transfer learning for punctuation restoration. arXiv preprint arXiv:2004.00248 (2020)"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"\u017belasko, P., Szyma\u0144ski, P., Mizgajski, J., Szymczak, A., Carmiel, Y., Dehak, N.: Punctuation prediction model for conversational speech. arXiv preprint arXiv:1807.00543 (2018)","DOI":"10.21437\/Interspeech.2018-1096"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Liu, X., Fu, J.: Neural networks incorporating dictionaries for chinese word segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5682\u20135689 (2018)","DOI":"10.1609\/aaai.v32i1.11959"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, J., Chi, L., Chen, X.: Word-level bert-CNN-RNN model for chinese punctuation restoration. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC) (2020)","DOI":"10.1109\/ICCC51575.2020.9344889"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wu, L., Cheng, S., Wang, M.: Unified multimodal punctuation restoration framework for mixed-modality corpus. In: 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022), pp. 7272\u20137276. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9747131"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8540-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T18:02:37Z","timestamp":1703440957000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8540-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,25]]},"ISBN":["9789819985395","9789819985401"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8540-1_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,25]]},"assertion":[{"value":"25 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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)"}}]}}