{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:58:55Z","timestamp":1772823535380,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the 2023 Southwest Jiaotong University International Student Education Management Research Project","award":["23LXSGL01"],"award-info":[{"award-number":["23LXSGL01"]}]},{"name":"the 2023 Southwest Jiaotong University International Student Education Management Research Project","award":["23LXSGL01"],"award-info":[{"award-number":["23LXSGL01"]}]},{"name":"Sichuan Science and Technology Program","award":["2023YFG0354"],"award-info":[{"award-number":["2023YFG0354"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176221, 62272398"],"award-info":[{"award-number":["62176221, 62272398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176221, 62272398"],"award-info":[{"award-number":["62176221, 62272398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s13042-024-02285-2","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T01:01:44Z","timestamp":1721782904000},"page":"741-753","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Text semantic matching algorithm based on the introduction of external knowledge under contrastive learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Jie","family":"Hu","sequence":"first","affiliation":[]},{"given":"Yinglian","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Lishan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Qilei","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Teng","sequence":"additional","affiliation":[]},{"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"issue":"11","key":"2285_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3567592","volume":"55","author":"S Ranathunga","year":"2023","unstructured":"Ranathunga S, Lee E-SA, Prifti Skenduli M, Shekhar R, Alam M, Kaur R (2023) Neural machine translation for low-resource languages: a survey. ACM Comput Surv 55(11):1\u201337","journal-title":"ACM Comput Surv"},{"issue":"3","key":"2285_CR2","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1561\/1500000100","volume":"16","author":"Y Fan","year":"2022","unstructured":"Fan Y, Xie X, Cai Y, Chen J, Ma X, Li X, Zhang R, Guo J (2022) Pre-training methods in information retrieval. Found Trends\u00ae Inf Retriev 16(3):178\u2013317","journal-title":"Found Trends\u00ae Inf Retriev"},{"issue":"4","key":"2285_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3624733","volume":"56","author":"Y Deldjoo","year":"2023","unstructured":"Deldjoo Y, Nazary F, Ramisa A, Mcauley J, Pellegrini G, Bellogin A, Noia TD (2023) A review of modern fashion recommender systems. ACM Comput Surv 56(4):1\u201337","journal-title":"ACM Comput Surv"},{"key":"2285_CR4","unstructured":"Kenton JDM-WC, Toutanova LK (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4171\u20134186"},{"key":"2285_CR5","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I et al (2018) Improving language understanding by generative pre-training"},{"key":"2285_CR6","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":"2285_CR7","doi-asserted-by":"crossref","unstructured":"Liu W, Zhou P, Zhao Z, Wang Z, Ju Q, Deng H, Wang P (2020)K-BERT: Enabling language representation with knowledge graph. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 2901\u20132908","DOI":"10.1609\/aaai.v34i03.5681"},{"key":"2285_CR8","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 Inf Process Syst 30"},{"key":"2285_CR9","unstructured":"Chen MY, Jiang H, Yang Y (2022) Context enhanced short text matching using clickthrough data. arXiv preprint arXiv:2203.01849"},{"key":"2285_CR10","doi-asserted-by":"crossref","unstructured":"Xia T, Wang Y, Tian Y, Chang Y (2021) Using prior knowledge to guide BERT\u2019s attention in semantic textual matching tasks. In: Proceedings of the Web Conference, pp 2466\u20132475","DOI":"10.1145\/3442381.3449988"},{"issue":"1","key":"2285_CR11","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","volume":"4","author":"L Rasmy","year":"2021","unstructured":"Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D (2021) Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med 4(1):86","journal-title":"NPJ Digit Med"},{"key":"2285_CR12","unstructured":"Su J, Cao J, Liu W, Ou Y (2021) Whitening sentence representations for better semantics and faster retrieval. arXiv preprint arXiv:2103.15316"},{"key":"2285_CR13","first-page":"9912","volume":"33","author":"M Caron","year":"2020","unstructured":"Caron M, Misra I, Mairal J, Goyal P, Bojanowski P, Joulin A (2020) Unsupervised learning of visual features by contrasting cluster assignments. Adv Neural Inf Process Syst 33:9912\u20139924","journal-title":"Adv Neural Inf Process Syst"},{"key":"2285_CR14","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2019) Sentence-BERT: Sentence embeddings using siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 3982\u20133992","DOI":"10.18653\/v1\/D19-1410"},{"key":"2285_CR15","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang S, Li Y, Feng S, Tian H, Wu H, Wang H (2020) ERNIE 2.0: A continual pre-training framework for language understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 8968\u20138975","DOI":"10.1609\/aaai.v34i05.6428"},{"issue":"8","key":"2285_CR16","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8):9","journal-title":"OpenAI Blog"},{"key":"2285_CR17","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"2285_CR18","doi-asserted-by":"crossref","unstructured":"Koubaa A (2023) GPT-4 vs. GPT-3.5: A concise showdown","DOI":"10.36227\/techrxiv.22312330.v2"},{"key":"2285_CR19","doi-asserted-by":"crossref","unstructured":"Li B, Zhou H, He J, Wang M, Yang Y, Li L (2020) On the sentence embeddings from pre-trained language models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 9119\u20139130","DOI":"10.18653\/v1\/2020.emnlp-main.733"},{"key":"2285_CR20","doi-asserted-by":"crossref","unstructured":"Liu Z, Xiong C, Sun M, Liu Z (2018) Entity-duet neural ranking: Understanding the role of knowledge graph semantics in neural information retrieval. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 2395\u20132405","DOI":"10.18653\/v1\/P18-1223"},{"key":"2285_CR21","doi-asserted-by":"crossref","unstructured":"Wang Z, Wu Z, Agarwal D, Sun J (2022) MedCLIP: Contrastive learning from unpaired medical images and text. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"issue":"10","key":"2285_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3561970","volume":"55","author":"N Rethmeier","year":"2023","unstructured":"Rethmeier N, Augenstein I (2023) A primer on contrastive pretraining in language processing: methods, lessons learned, and perspectives. ACM Comput Surv 55(10):1\u201317","journal-title":"ACM Comput Surv"},{"key":"2285_CR23","doi-asserted-by":"crossref","unstructured":"Gao T, Yao X, Chen D (2021) SimCSE: Simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 6894\u20136910","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"2285_CR24","unstructured":"Wu X, Gao C, Zang L, Han J, Wang Z, Hu S (2022)ESimCSE: Enhanced sample building method for contrastive learning of unsupervised sentence embedding. In: Proceedings of the 29th International Conference on Computational Linguistics, pp 3898\u20133907"},{"key":"2285_CR25","doi-asserted-by":"crossref","unstructured":"Chuang Y-S, Dangovski R, Luo H, Zhang Y, Chang S, Solja\u010di\u0107 M, Li S-W, Yih S, Kim Y, Glass J (2022) DiffCSE: Difference-based contrastive learning for sentence embeddings. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4207\u20134218","DOI":"10.18653\/v1\/2022.naacl-main.311"},{"key":"2285_CR26","doi-asserted-by":"crossref","unstructured":"Liu J, Liu J, Wang Q, Wang J, Wu W, Xian Y, Zhao D, Chen K, Yan R (2023) RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 13785\u201313802","DOI":"10.18653\/v1\/2023.acl-long.771"},{"key":"2285_CR27","doi-asserted-by":"crossref","unstructured":"Chanchani S, Huang R (2023) Composition-contrastive learning for sentence embeddings. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 15836\u201315848","DOI":"10.18653\/v1\/2023.acl-long.882"},{"key":"2285_CR28","doi-asserted-by":"crossref","unstructured":"Zhou K, Zhang B, Zhao WX, Wen J-R (2022) Debiased contrastive learning of unsupervised sentence representations. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 6120\u20136130","DOI":"10.18653\/v1\/2022.acl-long.423"},{"key":"2285_CR29","unstructured":"Wu X, Gao C, Su Y, Han J, Wang Z, Hu S (2022) Smoothed contrastive learning for unsupervised sentence embedding. In: Proceedings of the 29th International Conference on Computational Linguistics, pp 4902\u20134906"},{"key":"2285_CR30","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1109\/TASLP.2024.3402087","volume":"32","author":"X Huang","year":"2024","unstructured":"Huang X, Peng H, Zou D, Liu Z, Li J, Liu K, Wu J, Su J, Yu PS (2024) CoSENT: Consistent sentence embedding via similarity ranking. IEEE\/ACM Trans Audio Speech Language Process 32:2800\u20132813","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"2285_CR31","doi-asserted-by":"crossref","unstructured":"Nishikawa S, Ri R, Yamada I, Tsuruoka Y, Echizen I (2022) EASE: Entity-aware contrastive learning of sentence embedding. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 3870\u20133885","DOI":"10.18653\/v1\/2022.naacl-main.284"},{"key":"2285_CR32","doi-asserted-by":"publisher","first-page":"3105","DOI":"10.1007\/s13042-023-01823-8","volume":"14","author":"L Wu","year":"2023","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 14:3105\u20133112","journal-title":"Int J Mach Learn Cybern"},{"key":"2285_CR33","doi-asserted-by":"crossref","unstructured":"Karimi A, Rossi L, Prati A (2021) AEDA: An easier data augmentation technique for text classification. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 2748\u20132754","DOI":"10.18653\/v1\/2021.findings-emnlp.234"},{"issue":"4","key":"2285_CR34","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1023\/B:BTTJ.0000047600.45421.6d","volume":"22","author":"H Liu","year":"2004","unstructured":"Liu H, Singh P (2004) ConceptNet\u2013a practical commonsense reasoning tool-kit. BT Technol J 22(4):211\u2013226","journal-title":"BT Technol J"},{"key":"2285_CR35","doi-asserted-by":"crossref","unstructured":"Cer D, Diab M, Agirre EE, Lopez-Gazpio I, Specia L (2017) SemEval-2017 Task 1: Semantic textual similarity multilingual and cross-lingual focused evaluation. In: The 11th International Workshop on Semantic Evaluation (SemEval-2017), pp 1\u201314","DOI":"10.18653\/v1\/S17-2001"},{"key":"2285_CR36","doi-asserted-by":"crossref","unstructured":"Le HT, Cao DT, Bui TH, Luong LT, Nguyen HQ (2021) Improve quora question pair dataset for question similarity task. In: 2021 RIVF International Conference on Computing and Communication Technologies (RIVF), pp 1\u20135","DOI":"10.1109\/RIVF51545.2021.9642071"},{"key":"2285_CR37","unstructured":"Dolan B, Brockett C (2005) Automatically constructing a corpus of sentential paraphrases. In: 3rd International Workshop on Paraphrasing (IWP2005)"},{"key":"2285_CR38","doi-asserted-by":"crossref","unstructured":"Lan W, Qiu S, He H, Xu W (2017)A continuously growing dataset of sentential paraphrases. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 1224\u20131234","DOI":"10.18653\/v1\/D17-1126"},{"key":"2285_CR39","doi-asserted-by":"crossref","unstructured":"Jin Q, Dhingra B, Liu Z, Cohen W, Lu X (2019) PubMedQA: A dataset for biomedical research question answering. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 2567\u20132577","DOI":"10.18653\/v1\/D19-1259"},{"key":"2285_CR40","unstructured":"Tianchi (2020) New crown epidemic question sentence judgment dataset. https:\/\/tianchi.aliyun.com\/dataset\/dataDetail?dataId=76751"},{"key":"2285_CR41","doi-asserted-by":"crossref","unstructured":"Zhang N, Chen M, Bi Z, Liang X, Li L, Shang X, Yin K, Tan, C, Xu J, Huang F (2022) CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 7888\u20137915","DOI":"10.18653\/v1\/2022.acl-long.544"},{"key":"2285_CR42","doi-asserted-by":"crossref","unstructured":"Chen Q, Zhu X, Ling Z-H, Wei S, Jiang H, Inkpen D (2017) Enhanced LSTM for natural language inference. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1657\u20131668","DOI":"10.18653\/v1\/P17-1152"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02285-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02285-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02285-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T07:42:16Z","timestamp":1737531736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02285-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,24]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["2285"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02285-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,24]]},"assertion":[{"value":"10 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":3,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}