{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T04:09:44Z","timestamp":1748750984543,"version":"3.41.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["KMP6-0096055"],"award-info":[{"award-number":["KMP6-0096055"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s00521-024-10589-1","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T11:41:34Z","timestamp":1733398894000},"page":"10577-10590","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DACL+: domain-adapted contrastive learning for enhanced low-resource language representations in document clustering tasks"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-7060","authenticated-orcid":false,"given":"Dimitrios","family":"Zaikis","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3477-8825","authenticated-orcid":false,"given":"Ioannis","family":"Vlahavas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"10589_CR1","doi-asserted-by":"publisher","first-page":"58","DOI":"10.26555\/jifo.v14i2.a17513","volume":"14","author":"M Salih","year":"2020","unstructured":"Salih M, Jacksi NK (2020) State of the art document clustering algorithms based on semantic similarity. J Inform 14:58\u201375. https:\/\/doi.org\/10.26555\/jifo.v14i2.a17513","journal-title":"J Inform"},{"key":"10589_CR2","doi-asserted-by":"publisher","first-page":"101785","DOI":"10.1016\/j.is.2021.101785","volume":"100","author":"A Omar","year":"2021","unstructured":"Omar A, Mahmoud TM, Abd-El-Hafeez T, Mahfouz A (2021) Multi-label arabic text classification in online social networks. Inf Syst 100:101785. https:\/\/doi.org\/10.1016\/j.is.2021.101785","journal-title":"Inf Syst"},{"issue":"4","key":"10589_CR3","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3390\/bdcc2040032","volume":"2","author":"U Kokate","year":"2018","unstructured":"Kokate U, Deshpande A, Mahalle P, Patil P (2018) Data stream clustering techniques, applications, and models: comparative analysis and discussion. Big Data Cogn Comput 2(4):32. https:\/\/doi.org\/10.3390\/bdcc2040032","journal-title":"Big Data Cogn Comput"},{"issue":"1","key":"10589_CR4","doi-asserted-by":"publisher","first-page":"226","DOI":"10.3390\/su14010226","volume":"14","author":"A Badawy","year":"2021","unstructured":"Badawy A, Fisteus JA, Mahmoud TM, Abd El-Hafeez T (2021) Topic extraction and interactive knowledge graphs for learning resources. Sustainability 14(1):226. https:\/\/doi.org\/10.3390\/su14010226","journal-title":"Sustainability"},{"issue":"2","key":"10589_CR5","doi-asserted-by":"publisher","first-page":"102034","DOI":"10.1016\/j.ipm.2019.04.002","volume":"57","author":"SA Curiskis","year":"2020","unstructured":"Curiskis SA, Drake B, Osborn TR, Kennedy PJ (2020) An evaluation of document clustering and topic modelling in two online social networks: twitter and reddit. Inf Process Manag 57(2):102034. https:\/\/doi.org\/10.1016\/j.ipm.2019.04.002","journal-title":"Inf Process Manag"},{"key":"10589_CR6","doi-asserted-by":"publisher","unstructured":"Lekea I, Karampelas,P. (2017)Are we really that close together? tracing and discussing similarities and differences between greek terrorist groups using cluster analysis. In: 2017 European intelligence and security informatics conference (EISIC), pp. 159\u2013162 . https:\/\/doi.org\/10.1109\/EISIC.2017.33","DOI":"10.1109\/EISIC.2017.33"},{"issue":"4","key":"10589_CR7","doi-asserted-by":"publisher","first-page":"170","DOI":"10.3390\/bdcc7040170","volume":"7","author":"AME Koshiry","year":"2023","unstructured":"Koshiry AME, Eliwa EHI, Abd El-Hafeez T, Omar A (2023) Arabic toxic tweet classification: leveraging the arabert model. Big Data Cogn Comput 7(4):170. https:\/\/doi.org\/10.3390\/bdcc7040170","journal-title":"Big Data Cogn Comput"},{"key":"10589_CR8","doi-asserted-by":"crossref","unstructured":"Zaikis D, Kokkas S, Vlahavas I (2023) Dacl: A domain-adapted contrastive learning approach to low resource language representations for document clustering tasks. In: Iliadis L, Maglogiannis I, Alonso S, Jayne C, Pimenidis E (eds) Engineering applications of neural networks. Springer, Cham, pp 585\u2013598","DOI":"10.1007\/978-3-031-34204-2_47"},{"key":"10589_CR9","doi-asserted-by":"crossref","unstructured":"Vardakas G, Likas A (2022) Implicit maximum likelihood clustering. In: Maglogiannis I, Iliadis L, Macintyre J, Cortez P (eds) Artificial intelligence applications and innovations. Springer, Cham, pp 484\u2013495","DOI":"10.1007\/978-3-031-08337-2_40"},{"key":"10589_CR10","doi-asserted-by":"crossref","unstructured":"Vardakas G, Likas A (2023) Neural clustering based on implicit maximum likelihood. Neural computing and applications, 1\u201314","DOI":"10.1007\/s00521-023-08524-x"},{"key":"10589_CR11","doi-asserted-by":"crossref","unstructured":"Mukherjee S, Asnani H, Lin E, Kannan S (2019) Clustergan: Latent space clustering in generative adversarial networks. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 4610\u20134617","DOI":"10.1609\/aaai.v33i01.33014610"},{"key":"10589_CR12","doi-asserted-by":"publisher","unstructured":"Bollegala D, Maehara T, Kawarabayashi K-I (2015) Unsupervised cross-domain word representation learning 1. https:\/\/doi.org\/10.3115\/v1\/P15-1071","DOI":"10.3115\/v1\/P15-1071"},{"issue":"12","key":"10589_CR13","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","volume":"41","author":"M Long","year":"2019","unstructured":"Long M, Cao Y, Cao Z, Wang J, Jordan MI (2019) Transferable representation learning with deep adaptation networks. IEEE Trans Pattern Anal Mach Intell 41(12):3071\u20133085. https:\/\/doi.org\/10.1109\/TPAMI.2018.2868685","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10589_CR14","unstructured":"Long M, Cao Y, Wang J, Jordan M.I (2015) Learning transferable features with deep adaptation networks. In: Proceedings of the 32nd international conference on machine learning, Vol 37. ICML\u201915, pp 97\u2013105. JMLR.org"},{"key":"10589_CR15","unstructured":"Faralli S, Navigli R (2012) A new minimally-supervised framework for domain word sense disambiguation. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pp. 1411\u20131422. Association for Computational Linguistics, Jeju Island, Korea (2012). https:\/\/aclanthology.org\/D12-1129"},{"key":"10589_CR16","doi-asserted-by":"publisher","unstructured":"Neelakantan A, Shankar J, Passos A, McCallum A . Efficient non-parametric estimation of multiple embeddings per word in vector space. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1059\u20131069. Association for computational linguistics, Doha, Qatar (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1113 . https:\/\/aclanthology.org\/D14-1113","DOI":"10.3115\/v1\/D14-1113"},{"key":"10589_CR17","unstructured":"Beltagy I, Peters ME, Cohan A (2020) Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150"},{"key":"10589_CR18","unstructured":"Ye Z, Guo Q, Gan Q, Qiu X, Zhang Z (2019) Bp-transformer: modelling long-range context via binary partitioning. CoRR abs\/1911.04070 1911.04070"},{"key":"10589_CR19","unstructured":"Child R, Gray S, Radford A, Sutskever I (2019) Generating long sequences with sparse transformers. CoRR abs\/1904.10509 1904.10509"},{"key":"10589_CR20","unstructured":"Xie Q, Dai Z, Hovy EH, Luong M, Le QV (2019) Unsupervised data augmentation. CoRR abs\/1904.12848 1904.12848"},{"key":"10589_CR21","unstructured":"Joshi M, Levy O, Weld DS, Zettlemoyer L (2019) BERT for coreference resolution: baselines and analysis. CoRR abs\/1908.09091 1908.09091"},{"key":"10589_CR22","doi-asserted-by":"publisher","unstructured":"Clark K, Khandelwal U, Levy O, Manning CD (2019) What does BERT look at? an analysis of BERT\u2019s attention. In: Proceedings of the 2019 ACL workshop BlackboxNLP: analyzing and interpreting neural networks for NLP, pp. 276\u2013286. Association for computational linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/W19-4828 . https:\/\/aclanthology.org\/W19-4828","DOI":"10.18653\/v1\/W19-4828"},{"key":"10589_CR23","doi-asserted-by":"publisher","unstructured":"Chen D, Fisch A, Weston J, Bordes A (2017) Reading Wikipedia to answer open-domain questions. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1870\u20131879. Association for Computational Linguistics, Vancouver, Canada. https:\/\/doi.org\/10.18653\/v1\/P17-1171 . https:\/\/aclanthology.org\/P17-1171","DOI":"10.18653\/v1\/P17-1171"},{"key":"10589_CR24","doi-asserted-by":"publisher","unstructured":"Tang H, Sun X, Jin B, Wang J, Zhang F, Wu W (2021) Improving document representations by generating pseudo query embeddings for dense retrieval, 5054\u20135064 https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.392","DOI":"10.18653\/v1\/2021.acl-long.392"},{"key":"10589_CR25","doi-asserted-by":"publisher","unstructured":"Reimers N, Gurevych I (2019) Sentence-BERT: sentence embeddings using siamese BERT-networks, 3982\u20133992 https:\/\/doi.org\/10.18653\/v1\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"10589_CR26","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.jss.2016.06.012","volume":"127","author":"N Tsirakis","year":"2017","unstructured":"Tsirakis N, Poulopoulos V, Tsantilas P, Varlamis I (2017) Large scale opinion mining for social, news and blog data. J Syst Softw 127:237\u2013248. https:\/\/doi.org\/10.1016\/j.jss.2016.06.012","journal-title":"J Syst Softw"},{"key":"10589_CR27","doi-asserted-by":"publisher","first-page":"115951","DOI":"10.1109\/ACCESS.2023.3326337","volume":"11","author":"D Zaikis","year":"2023","unstructured":"Zaikis D, Vlahavas I (2023) From pre-training to meta-learning: a journey in low-resource-language representation learning. IEEE Access 11:115951\u2013115967. https:\/\/doi.org\/10.1109\/ACCESS.2023.3326337","journal-title":"IEEE Access"},{"key":"10589_CR28","doi-asserted-by":"crossref","unstructured":"Koutsikakis J, Chalkidis I, Malakasiotis P, Androutsopoulos I (2020) Greek-bert: the greeks visiting sesame street. In: 11th Hellenic conference on artificial intelligence, pp. 110\u2013117","DOI":"10.1145\/3411408.3411440"},{"key":"10589_CR29","doi-asserted-by":"crossref","unstructured":"Gururangan S, Marasovi\u0107 A, Swayamdipta S, Lo K, Beltagy I, Downey D., Smith N.A (2020) Don\u2019t Stop Pretraining: Adapt Language Models to Domains and Tasks. arXiv. arXiv:2004.10964 [cs]. http:\/\/arxiv.org\/abs\/2004.10964 Accessed 2022-09-12","DOI":"10.18653\/v1\/2020.acl-main.740"},{"issue":"1","key":"10589_CR30","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen J (1960) A coefficient of agreement for nominal scales. Edu Psychol Meas 20(1):37\u201346. https:\/\/doi.org\/10.1177\/001316446002000104","journal-title":"Edu Psychol Meas"},{"key":"10589_CR31","doi-asserted-by":"publisher","unstructured":"Cohan A, Feldman S, Beltagy I, Downey D, Weld DS (2020) SPECTER: document-level representation learning using citation-informed transformers. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2004.07180 . https:\/\/arxiv.org\/abs\/2004.07180","DOI":"10.48550\/ARXIV.2004.07180"},{"key":"10589_CR32","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A.N, Kaiser L, Polosukhin I (2017) Attention is All you Need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? . https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"10589_CR33","doi-asserted-by":"crossref","unstructured":"Wang K, Reimers N, Gurevych I (2021) TSDAE: using transformer-based sequential denoising auto-encoder for unsupervised sentence embedding learning","DOI":"10.18653\/v1\/2021.findings-emnlp.59"},{"key":"10589_CR34","doi-asserted-by":"publisher","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) FaceNet: A unified embedding for face recognition and clustering. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE. https:\/\/doi.org\/10.1109\/cvpr.2015.7298682","DOI":"10.1109\/cvpr.2015.7298682"},{"key":"10589_CR35","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1613\/jair.1523","volume":"22","author":"G Erkan","year":"2004","unstructured":"Erkan G, Radev DR (2004) Lexrank: graph-based lexical centrality as salience in text summarization. J Artif Intell Res 22:457\u2013479","journal-title":"J Artif Intell Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10589-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10589-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10589-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T09:16:44Z","timestamp":1748683004000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10589-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"references-count":35,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["10589"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10589-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,12,5]]},"assertion":[{"value":"2 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}