{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:50:09Z","timestamp":1762300209745,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031208645"},{"type":"electronic","value":"9783031208652"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20865-2_22","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:29:12Z","timestamp":1667518152000},"page":"297-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SICM: A Supervised-Based Identification and\u00a0Classification Model for\u00a0Chinese Jargons Using Feature Adapter Enhanced BERT"],"prefix":"10.1007","author":[{"given":"Yifei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Haochen","family":"Su","sequence":"additional","affiliation":[]},{"given":"Yingao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Haizhou","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"3504","DOI":"10.1109\/TASLP.2021.3124365","volume":"29","author":"Y Cui","year":"2021","unstructured":"Cui, Y., Che, W., Liu, T., Qin, B., Yang, Z.: Pre-training with whole word masking for Chinese BERT. IEEE\/ACM Trans. Audio Speech Lang. Process. 29, 3504\u20133514 (2021)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Ding, R., Xie, P., Zhang, X., Lu, W., Li, L., Si, L.: A neural multi-digraph model for Chinese NER with gazetteers. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1462\u20131467 (2019)","DOI":"10.18653\/v1\/P19-1141"},{"key":"22_CR3","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Ke, L., Chen, X., Wang, H.: An unsupervised detection framework for Chinese jargons in the darknet. In: Proceedings of the 15th ACM International Conference on Web Search and Data Mining, pp. 458\u2013466 (2022)","DOI":"10.1145\/3488560.3498469"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Kovalchuk, O., Masonkova, M., Banakh, S.: The dark web worldwide 2020: anonymous vs safety. In: Proceedings of the 11th International Conference on Advanced Computer Information Technologies, pp. 526\u2013530 (2021)","DOI":"10.1109\/ACIT52158.2021.9548578"},{"key":"22_CR6","unstructured":"Li, J., Meng, K.: MFE-NER: multi-feature fusion embedding for Chinese named entity recognition. arXiv preprint arXiv:2109.07877 (2021)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Li, X., Yan, H., Qiu, X., Huang, X.J.: Flat: Chinese NER using flat-lattice transformer. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6836\u20136842 (2020)","DOI":"10.18653\/v1\/2020.acl-main.611"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Liu, W., Fu, X., Zhang, Y., Xiao, W.: Lexicon enhanced Chinese sequence labeling using BERT adapter. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp. 5847\u20135858 (2021)","DOI":"10.18653\/v1\/2021.acl-long.454"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Ma, R., Peng, M., Zhang, Q., Wei, Z., Huang, X.J.: Simplify the usage of lexicon in Chinese NER. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5951\u20135960 (2020)","DOI":"10.18653\/v1\/2020.acl-main.528"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Song, Y., Shi, S., Li, J., Zhang, H.: Directional skip-gram: explicitly distinguishing left and right context for word embeddings. In: Proceedings of the 16th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 175\u2013180 (2018)","DOI":"10.18653\/v1\/N18-2028"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Strubell, E., Verga, P., Belanger, D., McCallum, A.: Fast and accurate entity recognition with iterated dilated convolutions. In: Proceedings of the 22nd Conference on Empirical Methods in Natural Language Processing, pp. 2670\u20132680 (2017)","DOI":"10.18653\/v1\/D17-1283"},{"key":"22_CR12","unstructured":"Takuro, H., Yuichi, S., Tahara, Y., Ohsuga, A.: Codewords detection in microblogs focusing on differences in word use between two corpora. In: Proceedings of the 3rd International Conference on Computing, Electronics and Communications Engineering, pp. 103\u2013108 (2020)"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Wang, H., Hou, Y., Wang, H.: A novel framework of identifying Chinese jargons for telegram underground markets. In: Proceedings of the 30th International Conference on Computer Communications and Networks, pp. 1\u20139 (2021)","DOI":"10.1109\/ICCCN52240.2021.9522221"},{"key":"22_CR14","unstructured":"Wang, R., et al.: K-adapter: infusing knowledge into pre-trained models with adapters. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp. 1405\u20131418 (2021)"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Yang, H., et al.: How to learn Klingon without a dictionary: detection and measurement of black keywords used by the underground economy. In: Proceedings of the 38th IEEE Symposium on Security and Privacy, pp. 751\u2013769 (2017)","DOI":"10.1109\/SP.2017.11"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Yang, P., Fang, H., Lin, J.: Anserini: enabling the use of Lucene for information retrieval research. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1253\u20131256 (2017)","DOI":"10.1145\/3077136.3080721"},{"key":"22_CR17","unstructured":"Yuan, K., Lu, H., Liao, X., Wang, X.: Reading thieves\u2019 cant: automatically identifying and understanding dark jargons from cybercrime marketplaces. In: Proceedings of the 27th USENIX Security Symposium, pp. 1027\u20131041 (2018)"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Zhou, X., Pan, S., Zhu, Q., Song, Z., Guo, L.: A relation-specific attention network for joint entity and relation extraction. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence, pp. 4054\u20134060 (2020)","DOI":"10.24963\/ijcai.2020\/561"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1554\u20131564 (2018)","DOI":"10.18653\/v1\/P18-1144"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, K., Zhang, Y., Xing, C., Li, W., Chen, H.: Chinese underground market jargon analysis based on unsupervised learning. In: Proceedings of the 14th IEEE Conference on Intelligence and Security Informatics, pp. 97\u2013102 (2016)","DOI":"10.1109\/ISI.2016.7745450"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, W., Bhat, S.: Euphemistic phrase detection by masked language model. In: Proceedings of the 26th Conference on Empirical Methods in Natural Language Processing, pp. 163\u2013168 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.16"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, W., et al.: Self-supervised euphemism detection and identification for content moderation. In: Proceedings of the 42nd IEEE Symposium on Security and Privacy, pp. 229\u2013246 (2021)","DOI":"10.1109\/SP40001.2021.00075"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2022: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20865-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:50:12Z","timestamp":1667519412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20865-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208645","9783031208652"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20865-2_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shangai","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pricai.org\/2022\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"432","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":"91","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":"39","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":"21% - 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":"7-8","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":"n\/a","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}