{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T02:31:02Z","timestamp":1745980262076,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":10,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811982842"},{"type":"electronic","value":"9789811982859"}],"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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":343,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Accurate identification of Internet buzzwords plays an important role in positive Internet opinion guidance. A Transformer-based Internet buzzword feature recognition system was designed to address this problem. The traditional way of crawling data has been improved, a real-time crawling module has been added, and an Internet buzzword corpus has been constructed by itself. The traditional way of crawling data has been improved, a real-time crawling module has been added, and an Internet buzzword corpus has been constructed by itself. Traditional machine learning models suffer from gradient disappearance and gradient explosion, the Transformer model, with its parallel computing and self-attentive mechanism, is a good solution to these problems, and its bi-directional connection allows the parameters of the context to be updated uniformly, thus allowing better aggregation of information and solving the problem of scattered contextual information. Transformation of the position-encoded part of the Transformer model starts with a relative position representation (RPR). It compensates for its inability to obtain relative location information. The experimental results show that the improved Transformer model can achieve an accuracy rate of 90.1%, a recall rate of 92.13%, and an F1 value of 91.16% in recognizing Internet buzzwords.<\/jats:p>","DOI":"10.1007\/978-981-19-8285-9_17","type":"book-chapter","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T20:02:48Z","timestamp":1670616168000},"page":"227-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on the Recognition of Internet Buzzword Features Based on Transformer"],"prefix":"10.1007","author":[{"given":"Dawei","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijie","family":"She","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonghua","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiguang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"17_CR1","unstructured":"The 49th Statistical Report on the Development of the Internet in China. China Internet Network Information Center (2022)"},{"key":"17_CR2","unstructured":"Qiu, X.P.: Neural Networks and Deep Learning. China Machine Press, Beijing (2020)"},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","volume":"34","author":"J Li","year":"2020","unstructured":"Li, J., Sun, A., Han, J., et al.: A survey on deep learning for named entity recognition. IEEE Trans. Knowl. Data Eng. 34, 50\u201370 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Hammerton, J.: Named entity recognition with long short-term memory. In: North American Chapter of the Association for Computational Linguistics, pp. 172\u2013175 (2003)","DOI":"10.3115\/1119176.1119202"},{"key":"17_CR5","unstructured":"Huang, Z., Xu, W., Yu, K., et al.: Bidirectional LSTM-CRF models for sequence tagging. Comput. Lang. (2015)"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv: Learning (2016)","DOI":"10.18653\/v1\/P16-1101"},{"issue":"2","key":"17_CR7","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P.Y., Frasconi, P., et al.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Dai, Z., Yang, Z., Yang, Y., et al.: Transformer-XL: attentive language models beyond a fixed-length context (2019)","DOI":"10.18653\/v1\/P19-1285"},{"issue":"01","key":"17_CR9","first-page":"35","volume":"52","author":"LP Du","year":"2016","unstructured":"Du, L.P., Li, X.P., Yu, G., Liu, C.L., Liu, R.: New word discovery based on mutual information improvement algorithm for Chinese word separation system improvement. Beijing Univ. J. 52(01), 35\u201340 (2016)","journal-title":"Beijing Univ. J."},{"key":"17_CR10","unstructured":"Vaswani, A., Shazier, N., Parmar, N., et al.: Attention is all you need. Neural Inf. Process. Syst. 30 (2017)"}],"container-title":["Communications in Computer and Information Science","Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8285-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,24]],"date-time":"2022-12-24T00:04:31Z","timestamp":1671840271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8285-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811982842","9789811982859"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8285-9_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CNCERT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China Cyber Security Annual Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"16 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 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":"cncert2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/conf.cert.org.cn","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}