{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T04:03:26Z","timestamp":1750997006758,"version":"3.41.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"DOI":"10.1007\/s11063-025-11743-z","type":"journal-article","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:40:18Z","timestamp":1749271218000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MTCR: Method for Matching Texts Against Causal Relationship"],"prefix":"10.1007","volume":"57","author":[{"given":"XinYue","family":"Jiang","sequence":"first","affiliation":[]},{"given":"JingSong","family":"He","sequence":"additional","affiliation":[]},{"given":"Li","family":"Gu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,7]]},"reference":[{"key":"11743_CR1","unstructured":"Li Y, Li C, Guo J (2022) Adaptive feature discrimination and denoising for asymmetric text matching. In: Proceedings of the 29th international conference on computational linguistics, pp 1146\u20131156"},{"key":"11743_CR2","doi-asserted-by":"crossref","unstructured":"Deng Y, Zhang W, Pan SJ, Bing L (2023) Bidirectional generative framework for cross-domain aspect-based sentiment analysis. arXiv preprint arXiv:2305.09509","DOI":"10.18653\/v1\/2023.acl-long.686"},{"key":"11743_CR3","doi-asserted-by":"crossref","unstructured":"Zou Y, Liu H, Gui T, Wang J, Zhang Q, Tang M, Li H, Wang D (2022) Divide and conquer: text semantic matching with disentangled keywords and intents. arXiv preprint arXiv:2203.02898","DOI":"10.18653\/v1\/2022.findings-acl.287"},{"key":"11743_CR4","doi-asserted-by":"crossref","unstructured":"Wang H, Yu D (2023) Going beyond sentence embeddings: a token-level matching algorithm for calculating semantic textual similarity. In: Proceedings of the 61st annual meeting of the association for computational linguistics (vol 2: short papers), pp 563\u2013570","DOI":"10.18653\/v1\/2023.acl-short.49"},{"issue":"2","key":"11743_CR5","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1007\/s11227-022-04708-9","volume":"79","author":"R Kumar","year":"2023","unstructured":"Kumar R, Sharma S (2023) Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval. J Supercomput 79(2):2251\u20132280","journal-title":"J Supercomput"},{"key":"11743_CR6","doi-asserted-by":"crossref","unstructured":"Pang L, Lan Y, Cheng X (2021) Match-ignition: plugging pagerank into transformer for long-form text matching. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 1396\u20131405","DOI":"10.1145\/3459637.3482450"},{"key":"11743_CR7","doi-asserted-by":"crossref","unstructured":"Li D, Yang Y, Tang H, Liu J, Wang Q, Wang J, Xu T, Wu W, Chen E (2022) Virt: improving representation-based text matching via virtual interaction. In: Proceedings of the 2022 conference on empirical methods in natural language processing, pp 914\u2013925","DOI":"10.18653\/v1\/2022.emnlp-main.59"},{"key":"11743_CR8","unstructured":"P\u00e9rez-Iglesias J, P\u00e9rez-Ag\u00fcera JR, Fresno V, Feinstein YZ (2009) Integrating the probabilistic models bm25\/bm25f into lucene. arXiv preprint arXiv:0911.5046"},{"key":"11743_CR9","unstructured":"Rong X (2014) word2vec parameter learning explained. arXiv preprint arXiv:1411.2738"},{"key":"11743_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2021.114135","volume":"304","author":"J Sarzynska-Wawer","year":"2021","unstructured":"Sarzynska-Wawer J, Wawer A, Pawlak A, Szymanowska J, Stefaniak I, Jarkiewicz M, Okruszek L (2021) Detecting formal thought disorder by deep contextualized word representations. Psychiatry Res 304:114135","journal-title":"Psychiatry Res"},{"key":"11743_CR11","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"11743_CR12","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi L, Chiriatti M (2020) Gpt-3: its nature, scope, limits, and consequences. Minds Mach 30:681\u2013694","journal-title":"Minds Mach"},{"key":"11743_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108574","volume":"106","author":"Y Zuo","year":"2023","unstructured":"Zuo Y, Lu W, Peng X, Wang S, Zhang W, Qiao X (2023) Ducl: dual-stage contrastive learning framework for Chinese semantic textual matching. Comput Electr Eng 106:108574","journal-title":"Comput Electr Eng"},{"issue":"2","key":"11743_CR14","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1002\/asi.24731","volume":"74","author":"C Yu","year":"2023","unstructured":"Yu C, Xue H, An L, Li G (2023) A lightweight semantic-enhanced interactive network for efficient short-text matching. J Assoc Info Sci Technol 74(2):283\u2013300","journal-title":"J Assoc Info Sci Technol"},{"issue":"3","key":"11743_CR15","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1109\/TII.2022.3174715","volume":"19","author":"T Gao","year":"2022","unstructured":"Gao T, Yang J, Jiang S (2022) A novel fault detection model based on vector quantization sparse autoencoder for nonlinear complex systems. IEEE Trans Ind Info 19(3):2693\u20132704","journal-title":"IEEE Trans Ind Info"},{"key":"11743_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110282","volume":"263","author":"X Pu","year":"2023","unstructured":"Pu X, Yuan L, Leng J, Wu T, Gao X (2023) Lexical knowledge enhanced text matching via distilled word sense disambiguation. Knowl Based Syst 263:110282","journal-title":"Knowl Based Syst"},{"key":"11743_CR17","doi-asserted-by":"crossref","unstructured":"Jiang K, Zhao Y, Jin G, Zhang Z, Cui R (2023) Ketm: a knowledge-enhanced text matching method. In: 2023 International joint conference on neural networks (IJCNN), IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN54540.2023.10191337"},{"key":"11743_CR18","doi-asserted-by":"crossref","unstructured":"Wu L, Hu J, Teng F, Li T, Du S (2023) Text semantic matching with an enhanced sample building method based on contrastive learning. Int J Mach Learn Cybern pp 1\u20138","DOI":"10.1007\/s13042-023-01823-8"},{"key":"11743_CR19","doi-asserted-by":"crossref","unstructured":"Huang P-S, He X, Gao J, Deng L, Acero A, Heck L (2013) Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM international conference on information & knowledge management, pp 2333\u20132338","DOI":"10.1145\/2505515.2505665"},{"key":"11743_CR20","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"issue":"10","key":"11743_CR21","first-page":"1995","volume":"3361","author":"Y LeCun","year":"1995","unstructured":"LeCun Y, Bengio Y et al (1995) Convolutional networks for images, speech, and time series. Handb Brain Theory Neural Netw 3361(10):1995","journal-title":"Handb Brain Theory Neural Netw"},{"key":"11743_CR22","unstructured":"Hu B, Lu Z, Li H, Chen Q (2014) Convolutional neural network architectures for matching natural language sentences. Adv Neural Info Process Syst pp 27"},{"key":"11743_CR23","unstructured":"Liu Y, Sun C, Lin L, Wang X (2016) Learning natural language inference using bidirectional lstm model and inner-attention. arXiv preprint arXiv:1605.09090"},{"key":"11743_CR24","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Info Process Syst pp 30"},{"key":"11743_CR25","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"11743_CR26","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: a lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942"},{"key":"11743_CR27","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2019) Sentence-bert: sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084","DOI":"10.18653\/v1\/D19-1410"},{"key":"11743_CR28","unstructured":"Zhou X, Li C, Bu J, Yao C, Shi K, Yu Z, Yu Z (2020) Matching text with deep mutual information estimation. arXiv preprint arXiv:2003.11521"},{"key":"11743_CR29","unstructured":"Huang Z, Xu W, Yu K (2015) Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991"},{"key":"11743_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109913","volume":"133","author":"FJP Montalbo","year":"2023","unstructured":"Montalbo FJP (2023) Machine-based mosquito taxonomy with a lightweight network-fused efficient dual convnet with residual learning and knowledge distillation. Appl Soft Comput 133:109913","journal-title":"Appl Soft Comput"},{"key":"11743_CR31","unstructured":"Turc I, Chang M-W, Lee K, Toutanova K (2019) Well-read students learn better: on the importance of pre-training compact models. arXiv preprint arXiv:1908.08962"},{"key":"11743_CR32","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T (2019) Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108"},{"key":"11743_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103632","volume":"228","author":"DL Borza","year":"2023","unstructured":"Borza DL, Ileni TA, Marinescu AI, Darabant SA (2023) Teacher or supervisor? Effective online knowledge distillation via guided collaborative learning. Comput Vis Image Underst 228:103632","journal-title":"Comput Vis Image Underst"},{"key":"11743_CR34","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"11743_CR35","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2014) Fitnets: hints for thin deep nets. arXiv preprint arXiv:1412.6550"},{"key":"11743_CR36","doi-asserted-by":"crossref","unstructured":"Heo B, Lee M, Yun S, Choi JY (2019) Knowledge transfer via distillation of activation boundaries formed by hidden neurons. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 3779\u20133787","DOI":"10.1609\/aaai.v33i01.33013779"},{"key":"11743_CR37","doi-asserted-by":"crossref","unstructured":"Park W, Kim D, Lu Y, Cho M (2019) Relational knowledge distillation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3967\u20133976","DOI":"10.1109\/CVPR.2019.00409"},{"key":"11743_CR38","doi-asserted-by":"crossref","unstructured":"Passalis N, Tzelepi M, Tefas A (2020) Heterogeneous knowledge distillation using information flow modeling. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2339\u20132348","DOI":"10.1109\/CVPR42600.2020.00241"},{"key":"11743_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105560","volume":"117","author":"M Sepahvand","year":"2023","unstructured":"Sepahvand M, Abdali-Mohammadi F, Taherkordi A (2023) An adaptive teacher-student learning algorithm with decomposed knowledge distillation for on-edge intelligence. Eng Appl Artif Intell 117:105560","journal-title":"Eng Appl Artif Intell"},{"key":"11743_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119040","volume":"213","author":"RK Keser","year":"2023","unstructured":"Keser RK, Ayanzadeh A, Aghdam OA, Kilcioglu C, Toreyin BU, Ure NK (2023) Pursuhint: in search of informative hint points based on layer clustering for knowledge distillation. Expert Syst Appl 213:119040","journal-title":"Expert Syst Appl"},{"key":"11743_CR41","doi-asserted-by":"crossref","unstructured":"Chen J, Yang L, Raman K, Bendersky M, Yeh J-J, Zhou Y, Najork M, Cai D, Emadzadeh E (2020) Dipair: fast and accurate distillation for trillion-scale text matching and pair modeling. arXiv preprint arXiv:2010.03099","DOI":"10.18653\/v1\/2020.findings-emnlp.264"},{"key":"11743_CR42","doi-asserted-by":"crossref","unstructured":"Mihalcea R, Tarau P (2004) Textrank: bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language processing, pp 404\u2013411","DOI":"10.3115\/1220575.1220627"},{"key":"11743_CR43","doi-asserted-by":"crossref","unstructured":"Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250","DOI":"10.18653\/v1\/D16-1264"},{"key":"11743_CR44","doi-asserted-by":"crossref","unstructured":"Khot T, Sabharwal A, Clark P (2018) Scitail: a textual entailment dataset from science question answering. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.12022"},{"key":"11743_CR45","doi-asserted-by":"crossref","unstructured":"Yang Y, Yih W-T, Meek C (2015) Wikiqa: a challenge dataset for open-domain question answering. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2013\u20132018","DOI":"10.18653\/v1\/D15-1237"},{"key":"11743_CR46","unstructured":"Wang M, Smith NA, Mitamura T (2007) What is the jeopardy model? A quasi-synchronous grammar for qa. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), pp 22\u201332"},{"issue":"2","key":"11743_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103245","volume":"60","author":"Z Yang","year":"2023","unstructured":"Yang Z, Xu Y, Hu J, Dong S (2023) Generating knowledge aware explanation for natural language inference. Info Process Manag 60(2):103245","journal-title":"Info Process Manag"},{"key":"11743_CR48","doi-asserted-by":"crossref","unstructured":"Welde TM, Liao L (2023) Design and development of counting-based visual question answering model using heuristic-based feature selection with deep learning. Artif Intell Rev pp 1\u201330","DOI":"10.1007\/s10462-022-10385-0"},{"key":"11743_CR49","doi-asserted-by":"crossref","unstructured":"Tay Y, Tuan LA, Hui SC (2017) Compare, compress and propagate: enhancing neural architectures with alignment factorization for natural language inference. arXiv preprint arXiv:1801.00102","DOI":"10.18653\/v1\/D18-1185"},{"key":"11743_CR50","doi-asserted-by":"crossref","unstructured":"Cao Q, Trivedi H, Balasubramanian A, Balasubramanian N (2020) Deformer: decomposing pre-trained transformers for faster question answering. arXiv preprint arXiv:2005.00697","DOI":"10.18653\/v1\/2020.acl-main.411"},{"key":"11743_CR51","doi-asserted-by":"crossref","unstructured":"Ni J, \u00c1brego GH, Constant N, Ma J, Hall KB, Cer D, Yang Y (2021) Sentence-t5: scalable sentence encoders from pre-trained text-to-text models. arXiv preprint arXiv:2108.08877","DOI":"10.18653\/v1\/2022.findings-acl.146"},{"key":"11743_CR52","unstructured":"Humeau S, Shuster K, Lachaux M-A, Weston J (2019) Poly-encoders: transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring. arXiv preprint arXiv:1905.01969"},{"key":"11743_CR53","doi-asserted-by":"crossref","unstructured":"Qi F, Yang Y, Yi J, Cheng Z, Liu Z, Sun M (2022) Quoter: a benchmark of quote recommendation for writing. arXiv preprint arXiv:2202.13145 (2022)","DOI":"10.18653\/v1\/2022.acl-long.27"},{"key":"11743_CR54","doi-asserted-by":"crossref","unstructured":"Mao Z, Wang H, Du Y, Wong K-f (2023) Unitrec: a unified text-to-text transformer and joint contrastive learning framework for text-based recommendation. arXiv preprint arXiv:2305.15756","DOI":"10.18653\/v1\/2023.acl-short.100"},{"key":"11743_CR55","doi-asserted-by":"crossref","unstructured":"Qi T, Wu F, Wu C, Huang Y (2022) News recommendation with candidate-aware user modeling. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 1917\u20131921","DOI":"10.1145\/3477495.3531778"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-025-11743-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-025-11743-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-025-11743-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T05:57:54Z","timestamp":1750917474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-025-11743-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,7]]},"references-count":55,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["11743"],"URL":"https:\/\/doi.org\/10.1007\/s11063-025-11743-z","relation":{},"ISSN":["1573-773X"],"issn-type":[{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2025,6,7]]},"assertion":[{"value":"24 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"58"}}