{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T07:47:38Z","timestamp":1772264858813,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Thu Dau Mot University, Binh Duong, Vietnam","award":["DT21.1-069"],"award-info":[{"award-number":["DT21.1-069"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s00521-021-06563-w","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T20:02:46Z","timestamp":1635451366000},"page":"4321-4341","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["GOWSeqStream: an integrated sequential embedding and graph-of-words for short text stream clustering"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7291-4168","authenticated-orcid":false,"given":"Tham","family":"Vo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"6563_CR1","doi-asserted-by":"crossref","unstructured":"Ganguli I, Sil J, Sengupta N (2021) Nonparametric method of topic identification using granularity concept and graph-based modeling. Neural Comput Appl 1\u201321","DOI":"10.1007\/s00521-020-05662-4"},{"key":"6563_CR2","doi-asserted-by":"crossref","unstructured":"Hassani A, Iranmanesh A, Mansouri N (2021)Text mining using nonnegative matrix factorization and latent semantic analysis. Neural Comput Appl 1\u201322","DOI":"10.1007\/s00521-021-06014-6"},{"issue":"2","key":"6563_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3276473","volume":"18","author":"T Nakamura","year":"2019","unstructured":"Nakamura T, Shirakawa M, Hara T, Nishio S (2019) Wikipedia-based relatedness measurements for multilingual short text clustering. ACM Trans Asian Low-Resour Language Inf Process (TALLIP) 18(2):1\u201325","journal-title":"ACM Trans Asian Low-Resour Language Inf Process (TALLIP)"},{"issue":"6","key":"6563_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3402884","volume":"19","author":"YP Ruan","year":"2020","unstructured":"Ruan YP, Ling ZH, Zhu X (2020) Condition-transforming variational autoencoder for generating diverse short text conversations. ACM Trans Asian Low-Resour Language Inf Process (TALLIP) 19(6):1\u201313","journal-title":"ACM Trans Asian Low-Resour Language Inf Process (TALLIP)"},{"issue":"11","key":"6563_CR5","doi-asserted-by":"publisher","first-page":"3218","DOI":"10.1109\/TCYB.2017.2762344","volume":"48","author":"S Zhao","year":"2017","unstructured":"Zhao S, Gao Y, Ding G, Chua TS (2017) Real-time multimedia social event detection in microblog. IEEE Trans Cybernet 48(11):3218\u20133231","journal-title":"IEEE Trans Cybernet"},{"key":"6563_CR6","doi-asserted-by":"crossref","unstructured":"Pham P, Nguyen LT, Vo B, & Yun U (2021) Bot2Vec: a general approach of intra-community oriented representation learning for bot detection in different types of social networks. Inf Syst 101771","DOI":"10.1016\/j.is.2021.101771"},{"key":"6563_CR7","doi-asserted-by":"crossref","unstructured":"Blei DM, & Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd international conference on Machine learning","DOI":"10.1145\/1143844.1143859"},{"key":"6563_CR8","doi-asserted-by":"crossref","unstructured":"Amoualian H, Clausel M, Gaussier E, & Amini MR (2016) Streaming-lda: A copula-based approach to modeling topic dependencies in document streams. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/2939672.2939781"},{"key":"6563_CR9","doi-asserted-by":"crossref","unstructured":"Du N, Farajtabar M, Ahmed A, Smola AJ, & Song L (2015) Dirichlet-hawkes processes with applications to clustering continuous-time document streams. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/2783258.2783411"},{"key":"6563_CR10","doi-asserted-by":"crossref","unstructured":"Yin J and Wang J  (2015) A text clustering algorithm using an online clustering scheme for initialization. In: ACM International Conference on Knowledge Discovery and Data Mining","DOI":"10.1145\/2939672.2939841"},{"key":"6563_CR11","doi-asserted-by":"crossref","unstructured":"Zhao Y, Liang S, Ren Z, Ma J,  Yilmaz E, and de Rijke M (2016) Explainable user clustering in short text streams. In: International ACM conference on research and de- velopment in information retrieval","DOI":"10.1145\/2911451.2911522"},{"key":"6563_CR12","doi-asserted-by":"crossref","unstructured":"Liang S,  Yilmaz E, & Kanoulas E (2016) Dynamic clustering of streaming short documents. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/2939672.2939748"},{"key":"6563_CR13","doi-asserted-by":"crossref","unstructured":"Livieris IE, Stavroyiannis S, Iliadis L, Pintelas P (2021) Smoothing and stationarity enforcement framework for deep learning time-series forecasting. Neural Comput Appl 1\u201315","DOI":"10.1007\/s00521-021-06043-1"},{"key":"6563_CR14","doi-asserted-by":"crossref","unstructured":"Yin J, Chao D,  Liu Z, Zhang W, Yu X, Wang J (2018) Model-based clustering of short text streams. In: ACM international conference on knowledge discovery and data mining","DOI":"10.1145\/3219819.3220094"},{"key":"6563_CR15","doi-asserted-by":"crossref","unstructured":"Chen J, Gong Z, Liu W (2020) A Dirichlet process biterm-based mixture model for short text stream clustering. Appl Intell 1\u201311","DOI":"10.1007\/s10489-019-01606-1"},{"issue":"5","key":"6563_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3390092","volume":"19","author":"MSH Ameur","year":"2020","unstructured":"Ameur MSH, Belkebir R, Guessoum A (2020) Robust arabic text categorization by combining convolutional and recurrent neural networks. ACM Trans Asian Low-Resour Language Inf Process (TALLIP) 19(5):1\u201316","journal-title":"ACM Trans Asian Low-Resour Language Inf Process (TALLIP)"},{"key":"6563_CR17","doi-asserted-by":"crossref","unstructured":"Kumar J, Shao J, Uddin S, Ali W (2020)     An online semantic-enhanced dirichlet model for short text stream clustering. In: Proceedings of the 58th annual meeting of the association for computational linguistics","DOI":"10.18653\/v1\/2020.acl-main.70"},{"key":"6563_CR18","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ins.2019.07.048","volume":"504","author":"J Chen","year":"2019","unstructured":"Chen J, Gong Z, Liu W (2019) A nonparametric model for online topic discovery with word embeddings. Inf Sci 504:32\u201347","journal-title":"Inf Sci"},{"issue":"1","key":"6563_CR19","first-page":"1","volume":"19","author":"Y Liu","year":"2019","unstructured":"Liu Y, Che W, Wang Y, Zheng B, Qin B, Liu T (2019) Deep contextualized word embeddings for universal dependency parsing. ACM Trans Asian Low-Resour Language Inf Process (TALLIP) 19(1):1\u201317","journal-title":"ACM Trans Asian Low-Resour Language Inf Process (TALLIP)"},{"key":"6563_CR20","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint http:\/\/arxiv.org\/abs\/1301.3781"},{"key":"6563_CR21","doi-asserted-by":"crossref","unstructured":"Pirbhulal S, Pombo N, Felizardo V, Garcia N, Sodhro AH, Mukhopadhyay SC (2019)  Towards machine learning enabled security framework for iot-based healthcare. In: 2019 13th international conference on sensing technology (ICST), IEEE","DOI":"10.1109\/ICST46873.2019.9047745"},{"key":"6563_CR22","doi-asserted-by":"crossref","unstructured":"AHMAD Ijaz et al (2020) Machine learning meets communication networks: current trends and future challenges. IEEE Access 8:223418\u2013223460","DOI":"10.1109\/ACCESS.2020.3041765"},{"key":"6563_CR23","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.future.2019.01.031","volume":"96","author":"Y Lin","year":"2019","unstructured":"Lin Y, Jin X, Chen J, Sodhro AH, Pan Z (2019) An analytic computation-driven algorithm for decentralized multicore systems. Futur Gener Comput Syst 96:101\u2013110","journal-title":"Futur Gener Comput Syst"},{"key":"6563_CR24","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.future.2019.07.068","volume":"102","author":"R Talat","year":"2020","unstructured":"Talat R, Obaidat MS, Muzammal M, Sodhro AH, Luo Z, Pirbhulal S (2020) A decentralised approach to privacy preserving trajectory mining. Futur Gener Comput Syst 102:382\u2013392","journal-title":"Futur Gener Comput Syst"},{"key":"6563_CR25","doi-asserted-by":"crossref","unstructured":"Wang X, McCallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining","DOI":"10.1145\/1150402.1150450"},{"key":"6563_CR26","first-page":"2909","volume":"7","author":"X Wei","year":"2007","unstructured":"Wei X, Sun J, Wang X (2007) Dynamic mixture models for multiple time-series. IJCAI 7:2909\u20132914","journal-title":"IJCAI"},{"key":"6563_CR27","unstructured":"Iwata T, Watanabe S, Yamada T, Ueda N (2009) Topic tracking model for analyzing consumer purchase behavior. In: Twenty-first international joint conference on artificial intelligence"},{"key":"6563_CR28","doi-asserted-by":"crossref","unstructured":"Ahmed A, Xing E (2008) Dynamic non-parametric mixture models and the recurrent chinese restaurant process: with applications to evolutionary clustering. In: Proceedings of the 2008 SIAM international conference on data mining. Society for industrial and applied mathematics","DOI":"10.1137\/1.9781611972788.20"},{"key":"6563_CR29","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Philip SY, Han J, & Wang J (2003) in A framework for clustering evolving data streams. In: Proceedings 2003 VLDB conference","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"issue":"5\u20136","key":"6563_CR30","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/j.neunet.2005.06.008","volume":"18","author":"S Zhong","year":"2005","unstructured":"Zhong S (2005) Efficient streaming text clustering. Neural Netw 18(5\u20136):790\u2013798","journal-title":"Neural Netw"},{"key":"6563_CR31","doi-asserted-by":"crossref","unstructured":"Cao F, Estert M, Qian W, Zhou A (2006) Density-based clustering over an evolving data stream with noise. In: Proceedings of the 2006 SIAM international conference on data mining","DOI":"10.1137\/1.9781611972764.29"},{"key":"6563_CR32","doi-asserted-by":"crossref","unstructured":"Shou L, Wang Z, Chen K, Chen G (2013) Sumblr: continuous summarization of evolving tweet streams. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval","DOI":"10.1145\/2484028.2484045"},{"key":"6563_CR33","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993\u20131022","journal-title":"J Mach Learn Res"},{"issue":"2","key":"6563_CR34","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s10115-009-0241-z","volume":"24","author":"CC Aggarwal","year":"2010","unstructured":"Aggarwal CC, Philip SY (2010) On clustering massive text and categorical data streams. Knowl Inf Syst 24(2):171\u2013196","journal-title":"Knowl Inf Syst"},{"key":"6563_CR35","unstructured":"Yan X, Han J (2002) gspan: graph-based substructure pattern mining. In: Proceedings of IEEE international conference on data mining"},{"key":"6563_CR36","doi-asserted-by":"crossref","unstructured":"Huan J, Wang W, Prins J (2003) Efficient mining of frequent subgraphs in the presence of isomorphism. In: Third IEEE international conference on data mining","DOI":"10.1145\/1014052.1014123"},{"key":"6563_CR37","doi-asserted-by":"crossref","unstructured":"Duan T, Lou Q, Srihari SN, & Xie X (2019) Sequential embedding induced text clustering, a non-parametric bayesian approach. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining","DOI":"10.1007\/978-3-030-16142-2_6"},{"key":"6563_CR38","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K & Zettlemoyer L (2018) Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","DOI":"10.18653\/v1\/N18-1202"},{"key":"6563_CR39","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2018) 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"},{"key":"6563_CR40","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning (PMLR)"},{"key":"6563_CR41","doi-asserted-by":"crossref","unstructured":"Hoang VCD, Dinh D, Le Nguyen N, Ngo HQ (2007) A comparative study on vietnamese text classification methods. In: 2007 IEEE international conference on research, innovation and vision for the future","DOI":"10.1109\/RIVF.2007.369167"},{"key":"6563_CR42","doi-asserted-by":"crossref","unstructured":"Vu T, Nguyen DQ, Nguyen DQ, Dras M, Johnson M (2018) Vncorenlp: a Vietnamese natural language processing toolkit. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: demonstrations","DOI":"10.18653\/v1\/N18-5012"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06563-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06563-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06563-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T16:11:49Z","timestamp":1646237509000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06563-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,28]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["6563"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06563-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,28]]},"assertion":[{"value":"2 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}