{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:48:34Z","timestamp":1776120514360,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Natural Science Foundation","award":["nos. 61902324, 11426179, and 61872298"],"award-info":[{"award-number":["nos. 61902324, 11426179, and 61872298"]}]},{"name":"Science and Technology Program of Sichuan Province","award":["no. 2023YFD0424"],"award-info":[{"award-number":["no. 2023YFD0424"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11227-023-05592-7","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T12:44:11Z","timestamp":1693831451000},"page":"3382-3411","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["SiMaLSTM-SNP: novel semantic relatedness learning model preserving both Siamese networks and membrane computing"],"prefix":"10.1007","volume":"80","author":[{"given":"Xu","family":"Gu","sequence":"first","affiliation":[]},{"given":"Xiaoliang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Xianyong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yajun","family":"Du","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"key":"5592_CR1","doi-asserted-by":"publisher","unstructured":"Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv:1409.2329, https:\/\/doi.org\/10.48550\/arXiv.1409.2329","DOI":"10.48550\/arXiv.1409.2329"},{"key":"5592_CR2","doi-asserted-by":"publisher","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2015) LSTM: a search space odyssey. CoRR arXiv: abs\/1503.04069, https:\/\/doi.org\/10.1109\/TNNLS.2016.2582924","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"5592_CR3","doi-asserted-by":"publisher","unstructured":"Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. CoRR arXiv: abs\/1503.00075, https:\/\/doi.org\/10.48550\/arXiv.1503.00075","DOI":"10.48550\/arXiv.1503.00075"},{"key":"5592_CR4","doi-asserted-by":"crossref","unstructured":"Mueller J, Thyagarajan A (2016) Siamese recurrent architectures for learning sentence similarity. In: 30th AAAI conference on artificial intelligence, AAAI 2016, February 12, 2016\u2013February 17, 2016","DOI":"10.1609\/aaai.v30i1.10350"},{"key":"5592_CR5","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1006\/jcss.1999.1693","volume":"61","author":"G P\u0103un","year":"2000","unstructured":"P\u0103un G (2000) Computing with membranes. J Comput Syst Sci 61:108\u2013143. https:\/\/doi.org\/10.1006\/jcss.1999.1693","journal-title":"J Comput Syst Sci"},{"key":"5592_CR6","volume-title":"The Oxford handbook of membrane computing","year":"2010","unstructured":"P\u0103un G, Rozenberg G, Salomaa A (eds) (2010) The Oxford handbook of membrane computing. Oxford University Press, The Netherlands"},{"key":"5592_CR7","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1109\/BICTA.2010.5645192","volume":"71","author":"MIG P\u0103un","year":"2006","unstructured":"P\u0103un MIG, Yokomori T (2006) Spiking neural p systems. Fund Inform 71:279\u2013308. https:\/\/doi.org\/10.1109\/BICTA.2010.5645192","journal-title":"Fund Inform"},{"key":"5592_CR8","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.ins.2022.03.003","volume":"597","author":"X Chen","year":"2022","unstructured":"Chen X, Peng H, Wang J, Hao F (2022) Supervisory control of discrete event systems under asynchronous spiking neuron P systems. Inf Sci 597:253\u2013273. https:\/\/doi.org\/10.1016\/j.ins.2022.03.003","journal-title":"Inf Sci"},{"key":"5592_CR9","doi-asserted-by":"publisher","unstructured":"Liu Q, Long L, Peng H, Wang J, Yang Q, Song X, Riscos-Nunez A, Perez-Jimenez MJ (2021) Gated spiking neural p systems for time series forecasting. https:\/\/doi.org\/10.1109\/TNNLS.2021.3134792","DOI":"10.1109\/TNNLS.2021.3134792"},{"issue":"10","key":"5592_CR10","doi-asserted-by":"publisher","first-page":"2050008","DOI":"10.1142\/S0129065720500082","volume":"30","author":"H Peng","year":"2020","unstructured":"Peng H, Lv Z, Li B, Luo X, Wang J, Song X, Wang T, P\u00e9rez-Jim\u00e9nez MJ, Riscos-N\u00fa\u00f1ez A (2020) Nonlinear spiking neural P systems. Int J Neural Syst 30(10):2050008\u20131205000817. https:\/\/doi.org\/10.1142\/S0129065720500082","journal-title":"Int J Neural Syst"},{"key":"5592_CR11","doi-asserted-by":"publisher","first-page":"107656","DOI":"10.1016\/j.knosys.2021.107656","volume":"235","author":"Q Liu","year":"2022","unstructured":"Liu Q, Long L, Yang Q, Peng H, Wang J, Luo X (2022) Lstm-snp: a long short-term memory model inspired from spiking neural p systems. Knowl Based Syst 235:107656. https:\/\/doi.org\/10.1016\/j.knosys.2021.107656","journal-title":"Knowl Based Syst"},{"key":"5592_CR12","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: 1st International conference on learning representations, ICLR 2013, Scottsdale, Arizona, USA, May 2\u20134, 2013, Workshop track proceedings"},{"key":"5592_CR13","doi-asserted-by":"crossref","unstructured":"Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 conference on empirical methods in natural language processing, EMNLP 2015, Lisbon, Portugal, September 17\u201321, 2015","DOI":"10.18653\/v1\/D15-1166"},{"key":"5592_CR14","doi-asserted-by":"publisher","unstructured":"Liu B, Lane IR (2016) Attention-based recurrent neural network models for joint intent detection and slot filling. CoRR arXiv: abs\/1609.01454, https:\/\/doi.org\/10.48550\/arXiv.1609.01454","DOI":"10.48550\/arXiv.1609.01454"},{"issue":"21\u201322","key":"5592_CR15","doi-asserted-by":"publisher","first-page":"14593","DOI":"10.1007\/s11042-018-7143-6","volume":"79","author":"F Xiao","year":"2020","unstructured":"Xiao F, Liu B, Li R (2020) Pedestrian object detection with fusion of visual attention mechanism and semantic computation. Multimedia Tools Appl 79(21\u201322):14593\u201314607. https:\/\/doi.org\/10.1007\/s11042-018-7143-6","journal-title":"Multimedia Tools Appl"},{"key":"5592_CR16","doi-asserted-by":"crossref","unstructured":"Won K, Jang Y, Choi H, Shin S (2020) Semantic classification of emf-related literature using deep learning models with attention mechanism. In: 2020 Research in adaptive and convergent systems, RACS 2020, October 13, 2020\u2013October 16, 2020","DOI":"10.1145\/3400286.3418259"},{"key":"5592_CR17","unstructured":"Marelli M, Menini S, Baroni M, Bentivogli L, Bernardi R, Zamparelli R (2014) A SICK cure for the evaluation of compositional distributional semantic models. In: Proceedings of the ninth international conference on language resources and evaluation, LREC 2014, Reykjavik, Iceland, May 26\u201331, 2014"},{"key":"5592_CR18","doi-asserted-by":"crossref","unstructured":"Cer DM, Diab MT, A E, Gazpio IL, Specia L (2017) Semeval-2017 task 1: semantic textual similarity multilingual and crosslingual focused evaluation. In: Proceedings of the 11th international workshop on semantic evaluation, SemEval@ACL 2017, Vancouver, Canada, August 3\u20134, 2017","DOI":"10.18653\/v1\/S17-2001"},{"key":"5592_CR19","doi-asserted-by":"crossref","unstructured":"He H, Gimpel K, Lin J (2015) Multi-perspective sentence similarity modeling with convolutional neural networks. In: Proceedings of the 2015 conference on empirical methods in natural language processing, EMNLP 2015, Lisbon, Portugal, September 17\u201321, 2015","DOI":"10.18653\/v1\/D15-1181"},{"issue":"1","key":"5592_CR20","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s42452-019-1903-4","volume":"2","author":"F Lei","year":"2019","unstructured":"Lei F, Liu X, Dai Q, Ling BW (2019) Shallow convolutional neural network for image classification. SN Appl Sci 2(1):97. https:\/\/doi.org\/10.1007\/s42452-019-1903-4","journal-title":"SN Appl Sci"},{"key":"5592_CR21","unstructured":"Kiros R, Zhu Y, Salakhutdinov R, Zemel RS, Torralba A, Urtasun R, Fidler S (2015) Skip-thought vectors. In: 29th Annual conference on neural information processing systems, NIPS 2015, December 7, 2015\u2013December 12, 2015"},{"key":"5592_CR22","doi-asserted-by":"publisher","unstructured":"Wieting J, Kirkpatrick TB, Gimpel K, Neubig G (2019) Beyond BLEU: training neural machine translation with semantic similarity. CoRR arXiv: abs\/1909.06694, https:\/\/doi.org\/10.18653\/v1\/P19-1427","DOI":"10.18653\/v1\/P19-1427"},{"key":"5592_CR23","doi-asserted-by":"crossref","unstructured":"Lieto A, Moro D, Devoti F, Parera C, Lipari V, Bestagini P, Tubaro S (2019) \"hello? who am I talking to?\" A shallow CNN approach for human vs. bot speech classification. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2019, Brighton, United Kingdom, May 12\u201317, 2019","DOI":"10.1109\/ICASSP.2019.8682743"},{"key":"5592_CR24","doi-asserted-by":"publisher","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. CoRR arXiv: abs\/1310.4546, https:\/\/doi.org\/10.5555\/2999792.2999959","DOI":"10.5555\/2999792.2999959"},{"key":"5592_CR25","doi-asserted-by":"publisher","unstructured":"Lee S, Lee D, Jang S, Yu H (2022) Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers), ACL 2022, Dublin, Ireland, May 22\u201327, 2022, pp 5969\u20135979. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.412","DOI":"10.18653\/v1\/2022.acl-long.412"},{"key":"5592_CR26","doi-asserted-by":"publisher","first-page":"117832","DOI":"10.1016\/j.eswa.2022.117832","volume":"207","author":"H Li","year":"2022","unstructured":"Li H, Wang W, Liu Z, Niu Y, Wang H, Zhao S, Liao Y, Yang W, Liu X (2022) A novel locality-sensitive hashing relational graph matching network for semantic textual similarity measurement. Expert Syst Appl 207:117832. https:\/\/doi.org\/10.1016\/j.eswa.2022.117832","journal-title":"Expert Syst Appl"},{"key":"5592_CR27","doi-asserted-by":"publisher","unstructured":"Cho K, Van MB, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078, https:\/\/doi.org\/10.48550\/arXiv.1406.1078","DOI":"10.48550\/arXiv.1406.1078"},{"key":"5592_CR28","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. CoRR arXiv: abs\/1706.03762. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"5592_CR29","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR arXiv: abs\/1810.04805, https:\/\/doi.org\/10.48550\/arXiv.1810.04805","DOI":"10.48550\/arXiv.1810.04805"},{"key":"5592_CR30","doi-asserted-by":"publisher","first-page":"166395","DOI":"10.1109\/ACCESS.2021.3135807","volume":"9","author":"D Chandrasekaran","year":"2021","unstructured":"Chandrasekaran D, Mago V (2021) Comparative analysis of word embeddings in assessing semantic similarity of complex sentences. IEEE Access 9:166395\u2013166408. https:\/\/doi.org\/10.1109\/ACCESS.2021.3135807","journal-title":"IEEE Access"},{"key":"5592_CR31","doi-asserted-by":"publisher","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. CoRR arXiv: abs\/1907.11692, https:\/\/doi.org\/10.48550\/arXiv.1907.11692","DOI":"10.48550\/arXiv.1907.11692"},{"key":"5592_CR32","doi-asserted-by":"publisher","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. CoRR arXiv: abs\/1909.11942, https:\/\/doi.org\/10.48550\/arXiv.1909.11942","DOI":"10.48550\/arXiv.1909.11942"},{"key":"5592_CR33","doi-asserted-by":"publisher","first-page":"117084","DOI":"10.1016\/j.eswa.2022.117084","volume":"200","author":"T Wang","year":"2022","unstructured":"Wang T, Shi H, Liu W, Yan X (2022) A joint framenet and element focusing sentence-bert method of sentence similarity computation. Expert Syst Appl 200:117084. https:\/\/doi.org\/10.1016\/j.eswa.2022.117084","journal-title":"Expert Syst Appl"},{"issue":"5","key":"5592_CR34","doi-asserted-by":"publisher","first-page":"6131","DOI":"10.1007\/s11042-021-11771-6","volume":"81","author":"D Viji","year":"2022","unstructured":"Viji D, Revathy S (2022) A hybrid approach of weighted fine-tuned BERT extraction with deep siamese bi-LSTM model for semantic text similarity identification. Multimedia Tools Appl 81(5):6131\u20136157. https:\/\/doi.org\/10.1007\/s11042-021-11771-6","journal-title":"Multimedia Tools Appl"},{"key":"5592_CR35","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.neunet.2022.04.030","volume":"152","author":"L Long","year":"2022","unstructured":"Long L, Liu Q, Peng H, Wang J, Yang Q (2022) Multivariate time series forecasting method based on nonlinear spiking neural P systems and non-subsampled shearlet transform. Neural Netw 152:300\u2013310. https:\/\/doi.org\/10.1016\/j.neunet.2022.04.030","journal-title":"Neural Netw"},{"key":"5592_CR36","doi-asserted-by":"crossref","unstructured":"Saruladha K, Thirumagal E, Arthi J, Aghila G (2013) Manhattan based hybrid semantic similarity algorithm for geospatial ontologies. 15th International Conference on Asia-Pacific Digital Libraries, ICADL 2013, December 9, 2013 - December 11, 2013","DOI":"10.1007\/978-3-319-03599-4_1"},{"key":"5592_CR37","unstructured":"Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR 2005), 20\u201326 June 2005, San Diego, CA, USA"},{"key":"5592_CR38","doi-asserted-by":"crossref","unstructured":"Prechelt L (2012) Early stopping-but when? Neural Networks, Tricks of the Trade-Second Edition","DOI":"10.1007\/978-3-642-35289-8_5"},{"key":"5592_CR39","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1142\/S0218001493000339","volume":"7","author":"J Bromley","year":"1993","unstructured":"Bromley J, Bentz JW, Bottou L, Guyon I, LeCun Y, Moore C, S\u00e4ckinger E, Shah R (1993) Signature verification using A siamese time delay neural network. Int J Pattern Recognit Artif Intell 7:669\u2013688. https:\/\/doi.org\/10.1142\/S0218001493000339","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"5592_CR40","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/electronics8101084","volume":"8","author":"DH Lee","year":"2019","unstructured":"Lee DH (2019) Fully convolutional single-crop siamese networks for real-time visual object tracking. Electronics 8:10. https:\/\/doi.org\/10.3390\/electronics8101084","journal-title":"Electronics"},{"key":"5592_CR41","doi-asserted-by":"publisher","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, https:\/\/doi.org\/10.48550\/arXiv.1409.0473","DOI":"10.48550\/arXiv.1409.0473"},{"issue":"359367","key":"5592_CR42","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1098\/rspl.1896.0076","volume":"60","author":"K Pearson","year":"1896","unstructured":"Pearson K (1896) Mathematical contributions to the theory of evolution on a form of spurious correlation which may arise when indices are used in the measurement of organs. Proc Roy Soc Lond 60(359367):489\u2013498. https:\/\/doi.org\/10.1098\/rspl.1896.0076","journal-title":"Proc Roy Soc Lond"},{"issue":"1","key":"5592_CR43","doi-asserted-by":"publisher","first-page":"72","DOI":"10.2307\/1412159","volume":"15","author":"C Spearman","year":"1904","unstructured":"Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72\u2013101. https:\/\/doi.org\/10.2307\/1412159","journal-title":"Am J Psychol"},{"issue":"320","key":"5592_CR44","first-page":"1329","volume":"62","author":"SZ Levine","year":"1967","unstructured":"Levine SZ (1967) Some remarks on the coefficient of determination for the normal distribution. J Am Stat Assoc 62(320):1329\u20131333","journal-title":"J Am Stat Assoc"},{"key":"5592_CR45","doi-asserted-by":"crossref","unstructured":"Huang B, Bai Y, Zhou X (2021) hub at semeval-2021 task 2: word meaning similarity prediction model based on roberta and word frequency. In: Proceedings of the 15th international workshop on semantic evaluation, SemEval@ACL\/IJCNLP 2021, Virtual Event\/Bangkok, Thailand, August 5\u20136, 2021","DOI":"10.18653\/v1\/2021.semeval-1.94"},{"key":"5592_CR46","doi-asserted-by":"crossref","unstructured":"Lai A, Hockenmaier J (2014) Illinois-lh: a denotational and distributional approach to semantics. In: 8th International workshop on semantic evaluation, SemEval 2014, August 23, 2014\u2013August 24, 2014","DOI":"10.3115\/v1\/S14-2055"},{"key":"5592_CR47","doi-asserted-by":"crossref","unstructured":"Jimenez S, Duenas G, Baquero J, Gelbukh A (2014) Unal-nlp: Combining soft cardinality features for semantic textual similarity, relatedness and entailment. 8th International Workshop on Semantic Evaluation, SemEval 2014, August 23, 2014 - August 24, 2014","DOI":"10.3115\/v1\/S14-2131"},{"key":"5592_CR48","doi-asserted-by":"crossref","unstructured":"Zhao J, Zhu T, Lan M (2014) Ecnu: one stone two birds: ensemble of heterogenous measures for semantic relatedness and textual entailment. In: 8th International workshop on semantic evaluation, SemEval 2014, August 23, 2014\u2013August 24, 2014","DOI":"10.3115\/v1\/S14-2044"},{"key":"5592_CR49","doi-asserted-by":"crossref","unstructured":"Bjerva J, Bos J, Goot RVD, Nissim M (2014) The meaning factory: formal semantics for recognizing textual entailment and determining semantic similarity. In: 8th International workshop on semantic evaluation, SemEval 2014, August 23, 2014\u2013August 24, 2014","DOI":"10.3115\/v1\/S14-2114"},{"key":"5592_CR50","doi-asserted-by":"crossref","unstructured":"Proisl T, Evert S, Greiner P, Kabashi B (2014) Semantiklue: Robust semantic similarity at multiple levels using maximum weight matching. In: Proceedings of the 8th international workshop on semantic evaluation, SemEval@COLING 2014, Dublin, Ireland, August 23\u201324, 2014","DOI":"10.3115\/v1\/S14-2093"},{"key":"5592_CR51","doi-asserted-by":"crossref","unstructured":"Bestgen Y (2014) CECL: a new baseline and a non-compositional approach for the sick benchmark. In: Proceedings of the 8th international workshop on semantic evaluation, SemEval@COLING 2014, Dublin, Ireland, August 23\u201324, 2014","DOI":"10.3115\/v1\/S14-2024"},{"key":"5592_CR52","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1162\/tacl_a_00177","volume":"2","author":"R Socher","year":"2014","unstructured":"Socher R, Karpathy A, Le QV, Manning CD, Ng AY (2014) Grounded compositional semantics for finding and describing images with sentences. Trans Assoc Comput Linguist 2:207\u2013218. https:\/\/doi.org\/10.1162\/tacl_a_00177","journal-title":"Trans Assoc Comput Linguist"},{"key":"5592_CR53","doi-asserted-by":"publisher","unstructured":"Sutskever I, Vinyals O, Le Q (2014) Sequence to sequence learning with neural networks. Advances in neural information processing systems 27. https:\/\/doi.org\/10.5555\/2969033.2969173","DOI":"10.5555\/2969033.2969173"},{"issue":"10","key":"5592_CR54","doi-asserted-by":"publisher","first-page":"2216","DOI":"10.1587\/transinf.2018EDP7410","volume":"E103D","author":"DG Huang","year":"2020","unstructured":"Huang DG, Arafat AASY, Rashid KI, Abbas Q, Ren FJ (2020) Sentence-embedding and similarity via hybrid bidirectional-lstm and cnn utilizing weighted-pooling attention. IEICE Trans Inf Syst E103D(10):2216\u20132227. https:\/\/doi.org\/10.1587\/transinf.2018EDP7410","journal-title":"IEICE Trans Inf Syst"},{"key":"5592_CR55","doi-asserted-by":"crossref","unstructured":"Chen Y (2018) CT-LSTM: detection and estimation duplexed system for robust object tracking. In: The 2nd international conference on computer science and application engineering, CSAE 2018, Hohhot, China, October 22\u201324, 2018","DOI":"10.1145\/3207677.3277985"},{"key":"5592_CR56","doi-asserted-by":"crossref","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2020) BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5\u201310, 2020","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"5592_CR57","doi-asserted-by":"publisher","first-page":"104118","DOI":"10.1016\/j.jbi.2022.104118","volume":"131","author":"E Chang","year":"2022","unstructured":"Chang E (2022) A vector-based semantic relatedness measure using multiple relations within SNOMED CT and UMLS. J Biomed Inf 131:104118. https:\/\/doi.org\/10.1016\/j.jbi.2022.104118","journal-title":"J Biomed Inf"},{"key":"5592_CR58","doi-asserted-by":"publisher","unstructured":"Ethayarajh K, Duvenaud D, Hirst G (2018) Towards understanding linear word analogies. CoRR arXiv: abs\/1810.04882, https:\/\/doi.org\/10.18653\/v1\/P19-1315","DOI":"10.18653\/v1\/P19-1315"},{"key":"5592_CR59","doi-asserted-by":"publisher","unstructured":"Li B, Zhou H, He J, Wang M, Yang Y, Li L (2020) On the sentence embeddings from pre-trained language models. CoRR arXiv: abs\/2011.05864, https:\/\/doi.org\/10.48550\/arXiv.2011.05864","DOI":"10.48550\/arXiv.2011.05864"},{"key":"5592_CR60","doi-asserted-by":"publisher","unstructured":"Gao J, He D, Tan X, Qin T, Wang L, Liu T (2019) Representation degeneration problem in training natural language generation models. CoRR arXiv: abs\/1907.12009, https:\/\/doi.org\/10.48550\/arXiv.1907.12009","DOI":"10.48550\/arXiv.1907.12009"},{"key":"5592_CR61","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing, EMNLP 2014, October 25\u201329, 2014, Doha, Qatar, a meeting of SIGDAT, a Special Interest Group of the ACL","DOI":"10.3115\/v1\/D14-1181"},{"key":"5592_CR62","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An image is worth 16x16 words: transformers for image recognition at scale. CoRR arXiv: abs\/2010.11929, https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05592-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05592-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05592-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T11:07:03Z","timestamp":1705921623000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05592-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,4]]},"references-count":62,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["5592"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05592-7","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,4]]},"assertion":[{"value":"17 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethica approval"}}]}}