{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:15:54Z","timestamp":1782375354431,"version":"3.54.5"},"reference-count":18,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,4,22]]},"abstract":"<jats:p>Intent recognition is one of the most essential foundations as well as a very challenging task for language understanding, especially for spoken language. As spoken text is short, and lack of full context. Moreover, it may mix multi-language forms. These non-standard spoken expressions further lead to the shortage of text information. In consideration that sparse text information seriously affects the effect of intention understanding, a multi-feature fusion-based intent recognition model for the bilingual phenomenon mixed with Chinese and English is proposed. Combining word2vec and multilingual wordNets with the same synset_id (synonym set id), the model can mask the differences between different languages. Meanwhile, it can enrich the information representation of the spoken text by fusing the word intention features with the context-dependent features represented by transformer as well as the word frequency features. To verify the correctness and effectiveness of the model, extensive experiments were conducted on a real online logistics customer service platform and SMP2018-ECDT dataset. The results show that our model is superior to other models. And it improves the accuracy of intent recognition in logistics data by 20% compared with that of transformer.<\/jats:p>","DOI":"10.3233\/jifs-202365","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T12:14:26Z","timestamp":1611922466000},"page":"10261-10272","source":"Crossref","is-referenced-by-count":7,"title":["An intent recognition model supporting the spoken expression mixed with Chinese and English"],"prefix":"10.1177","volume":"40","author":[{"given":"Miao","family":"Hu","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"},{"name":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China"},{"name":"Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy for Engineering & Technology, Fudan University, Shanghai, China"},{"name":"School of Computer Science and Technology, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxiang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhe","family":"Qi","sequence":"additional","affiliation":[{"name":"Academy for Engineering & Technology, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-202365_ref3","first-page":"61","article-title":"Using maximum entropy for text classification","volume":"1","author":"Nigam","year":"1999","journal-title":"IJCAI-99 workshop on machine learning for information filtering"},{"key":"10.3233\/JIFS-202365_ref4","doi-asserted-by":"crossref","unstructured":"Wang Z.A. , Sun X. , Zhang D.X. and Li X. , An optimal SVM-based text classification algorithm, 2006 International Conference on Machine Learning and Cybernetics, IEEE, 2006, pp. 1378\u20131381.","DOI":"10.1109\/ICMLC.2006.258708"},{"key":"10.3233\/JIFS-202365_ref5","doi-asserted-by":"crossref","unstructured":"Hingmire S. , Chougule S. , Palshikar G.K. and Chakraborti S. , Document classification by topic labeling, Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013, pp. 877\u2013880.","DOI":"10.1145\/2484028.2484140"},{"key":"10.3233\/JIFS-202365_ref6","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.neucom.2015.03.021","article-title":"Stability analysis for delayed high-order type of Hopfield neural networks with impulses","volume":"165","author":"Arbi","year":"2015","journal-title":"Neurocomputing"},{"issue":"3","key":"10.3233\/JIFS-202365_ref8","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1002\/mma.4661","article-title":"Dynamics of BAM neural networks with mixed delays and leakage time-varying delays in the weighted pseudo\u2013almost periodic on time-space scales","volume":"41","author":"Arbi","year":"2018","journal-title":"Mathematical Methods in the Applied Sciences"},{"key":"10.3233\/JIFS-202365_ref9","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"WordNet: a lexical database for English","volume":"38","author":"Miller","year":"1995","journal-title":"Communications of the ACM"},{"key":"10.3233\/JIFS-202365_ref13","doi-asserted-by":"crossref","unstructured":"Lai S. , Xu L. , Liu K. and Zhao J. , Recurrent convolutional neural networks for text classification, In: Twenty-ninth AAAI conference on artificial intelligence, 2015.","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"10.3233\/JIFS-202365_ref15","doi-asserted-by":"crossref","first-page":"30885","DOI":"10.1109\/ACCESS.2020.2972751","article-title":"Hierarchical graph transformer-based deep learning model for large-scale multi-label text classification","volume":"8","author":"Gong","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-202365_ref16","unstructured":"Dai W. , Xue G.R. , Yang Q. and Yu Y. , Transferring naive bayes classifiers for text classification, National Conference on Artificial Intelligence, 2007."},{"key":"10.3233\/JIFS-202365_ref17","doi-asserted-by":"crossref","unstructured":"Yong Z. , Youwen L. and Shixiong X. , An Improved KNN Text Classification Algorithm Based on Clustering 4 (2009).","DOI":"10.4304\/jcp.4.3.230-237"},{"key":"10.3233\/JIFS-202365_ref19","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.neucom.2016.03.088","article-title":"Multi-label maximum entropy model for social emotion classification over short text","volume":"210","author":"Li","year":"2016","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-202365_ref20","doi-asserted-by":"crossref","first-page":"82","DOI":"10.15388\/NA.2018.1.7","article-title":"Improved synchronization analysis of competitive neural networks with time-varying delays","volume":"23.1","author":"Arbi","year":"2018","journal-title":"Nonlinear Anal Model Control"},{"issue":"4","key":"10.3233\/JIFS-202365_ref21","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3390\/data3040046","article-title":"Development of the non-iterative supervised learning predictor based on the ito decomposition and SGTM neural-like structure for managing medical insurance costs","volume":"3","author":"Tkachenko","year":"2018","journal-title":"Data"},{"key":"10.3233\/JIFS-202365_ref22","doi-asserted-by":"crossref","unstructured":"Tkachenko R. , Izonin I. and Tkachenko P. , Committee of the SGTM Neural-Like Structures with Extended Inputs for Predictive Analytics in Insurance, In International Conference on Big Data Innovations and Applications, Springer, 2019, pp. 121\u2013132.","DOI":"10.1007\/978-3-030-27355-2_9"},{"key":"10.3233\/JIFS-202365_ref26","doi-asserted-by":"crossref","unstructured":"Meng X. , Cui R. , Zhao Y. and Zhang Z. , Multilingual short text classification based on LDA and BiLSTM-CNN neural network, International Conference on Web Information Systems and Applications, Springer, 2019, pp. 319\u2013323.","DOI":"10.1007\/978-3-030-30952-7_32"},{"key":"10.3233\/JIFS-202365_ref27","unstructured":"Hanneman G. and Lavie A. , Automatic category label coarsening for syntax-based machine translation, Proceedings of Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation, 2011, pp. 98\u2013106."},{"key":"10.3233\/JIFS-202365_ref28","doi-asserted-by":"crossref","unstructured":"Amine B.M. and Mimoun M. , Wordnet based cross-language text categorization, In 2007 IEEE\/ACS International Conference on Computer Systems and Applications, 2007, pp. 848\u2013855.","DOI":"10.1109\/AICCSA.2007.370731"},{"key":"10.3233\/JIFS-202365_ref30","unstructured":"Liu X. , Chen Q. , Deng C. , Zeng H. , Cheng J. , Li D. and Tang B. , Lcqmc:A large-scale Chinese question matching corpus, Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 1952\u20131962."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-202365","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:41Z","timestamp":1777455701000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-202365"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,22]]},"references-count":18,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-202365","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,22]]}}}