{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T10:28:54Z","timestamp":1781087334910,"version":"3.54.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2018YFC0831700"],"award-info":[{"award-number":["2018YFC0831700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2018YFC0830705"],"award-info":[{"award-number":["2018YFC0830705"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502259"],"award-info":[{"award-number":["61502259"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong","doi-asserted-by":"publisher","award":["2020CXGC010901"],"award-info":[{"award-number":["2020CXGC010901"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong","doi-asserted-by":"publisher","award":["2019JZZY020124"],"award-info":[{"award-number":["2019JZZY020124"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11280-022-01037-y","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T08:03:14Z","timestamp":1651132994000},"page":"1703-1723","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-granularity interaction model based on pinyins and radicals for Chinese semantic matching"],"prefix":"10.1007","volume":"25","author":[{"given":"Pengyu","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenpeng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoujin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueping","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Jian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"1037_CR1","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, Q., Liu, X., Yang, H., Daohe, L u, Tang, B.: The BQ corpus: A large-scale domain-specific Chinese corpus for sentence semantic equivalence identification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 4946\u20134951 (2018)","DOI":"10.18653\/v1\/D18-1536"},{"key":"1037_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Q., Zhu, X., Ling, Zhen-Hua, Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 1657\u20131668 (2017)","DOI":"10.18653\/v1\/P17-1152"},{"key":"1037_CR3","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171\u20134186 (2019)"},{"key":"1037_CR4","doi-asserted-by":"crossref","unstructured":"Fei, H., Ren, Y., Ji, D.: Improving text understanding via deep syntax-semantics communication. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing: Findings, pp. 84\u201393 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.8"},{"key":"1037_CR5","doi-asserted-by":"crossref","unstructured":"He, Q., Wang, H., Zhang, Y.:\u00a0Enhancing generalization in natural language inference by syntax. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing: Findings, pp. 4973\u20134978 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.447"},{"key":"1037_CR6","doi-asserted-by":"crossref","unstructured":"Hu, H., Richardson, K., Xu, L., Li, L., K\u00fcbler, S., Moss, L.S: Ocnli: Original\u00a0Chinese\u00a0natural language inference. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing: Findings, pp. 3512\u20133526 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.314"},{"key":"1037_CR7","doi-asserted-by":"crossref","unstructured":"Huang, Q., Bu, J., Xie, W., Yang, S., Wu, W., Liu, L.: Multi-task sentence encoding model for semantic retrieval in question answering systems. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1\u20138 (2019)","DOI":"10.1109\/IJCNN.2019.8852327"},{"key":"1037_CR8","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1746\u20131751 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"1037_CR9","doi-asserted-by":"crossref","unstructured":"Kim, S., Kang, I., Kwak, N.: Semantic sentence matching with densely-connected recurrent and co-attentive information. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 6586\u20136593 (2019)","DOI":"10.1609\/aaai.v33i01.33016586"},{"key":"1037_CR10","unstructured":"Kingma, D.P., Adam, J.B.: A method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (2015)"},{"key":"1037_CR11","doi-asserted-by":"crossref","unstructured":"Lai, Y., Feng, Y., Yu, X., Wang, Z., Xu, K., Zhao, D.: Lattice CNNs for matching based Chinese question answering. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 6634\u20136641 (2019)","DOI":"10.1609\/aaai.v33i01.33016634"},{"key":"1037_CR12","doi-asserted-by":"crossref","unstructured":"Li, X., Meng, Y., Sun, X., Han, Q., Yuan, A., Li, J.: Is word segmentation necessary for deep learning of Chinese representations?. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 3242\u20133252 (2019)","DOI":"10.18653\/v1\/P19-1314"},{"key":"1037_CR13","unstructured":"Liu, X., Chen, Q., Deng, C., Zeng, H., Chen, J., Li, D., Tang, B.: LCQMC: A large-scale Chinese question matching corpus. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1952\u20131962 (2018)"},{"key":"1037_CR14","doi-asserted-by":"crossref","unstructured":"Liu, L., Yang, W., Rao, J., Tang, R., Lin, J.: Incorporating contextual and syntactic structures improves semantic similarity modeling. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1204\u20131209 (2019)","DOI":"10.18653\/v1\/D19-1114"},{"key":"1037_CR15","doi-asserted-by":"crossref","unstructured":"Liu, S., Yang, T., Yue, T., Zhang, F., Wang, D.: PLOME: Pre-training with misspelled knowledge for Chinese spelling correction. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp. 2991\u20133000 (2021)","DOI":"10.18653\/v1\/2021.acl-long.233"},{"key":"1037_CR16","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhang, Z., Zhao, H., Zhou, X., Zhou, X.: Filling the gap of utterance-aware and speaker-aware representation for multi-turn dialogue. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp. 13406\u201313414 (2021)","DOI":"10.1609\/aaai.v35i15.17582"},{"key":"1037_CR17","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhou, P., Zhao, Z., Wang, Z., Deng, H., Ju, Q.: FastBERT: A self-distilling BERT with adaptive inference time. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6035\u20136044 (2020)","DOI":"10.18653\/v1\/2020.acl-main.537"},{"issue":"2","key":"1037_CR18","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1007\/s11036-017-0932-8","volume":"23","author":"H Lu","year":"2018","unstructured":"Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications 23(2), 368\u2013375 (2018)","journal-title":"Mobile Networks and Applications"},{"issue":"1s","key":"1037_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3422668","volume":"17","author":"H Lu","year":"2021","unstructured":"Lu, H., Yang, R., Deng, Z., Zhang, Y., Gao, G., Lan, R.: Chinese image captioning via fuzzy attention-based densenet-BiLSTM. ACM Transactions on Multimedia Computing Communications, and Applications 17(1s), 1\u201318 (2021)","journal-title":"ACM Transactions on Multimedia Computing Communications, and Applications"},{"issue":"4","key":"1037_CR20","first-page":"1","volume":"21","author":"W Lu","year":"2021","unstructured":"Lu, W., Yu, R., Wang, S., Wang, C., Jian, P., Huang, H.: Sentence semantic matching based on 3D CNN for human\u2013robot language interaction. ACM Trans. Internet Technol. 21(4), 1\u201324 (2021)","journal-title":"ACM Trans. Internet Technol."},{"issue":"1","key":"1037_CR21","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MIS.2020.3021188","volume":"36","author":"W Lu","year":"2021","unstructured":"Lu, W., Zhang, Y., Wang, S., Huang, H., Liu, Q., Luo, S.: Concept representation by learning explicit and implicit concept couplings. IEEE Intell. Syst. 36(1), 6\u201315 (2021)","journal-title":"IEEE Intell. Syst."},{"issue":"1","key":"1037_CR22","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/TFUZZ.2020.2984991","volume":"29","author":"H Lu","year":"2021","unstructured":"Lu, H., Zhang, M., Xu, X., Li, Y., Shen, H.T.: Deep fuzzy hashing network for efficient image retrieval. IEEE Trans. Fuzzy Syst. 29(1), 166\u2013176 (2021)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"1037_CR23","unstructured":"Lu, C., Zhao, Y., Lyu, B., Jin, L., Chen, Z., Zhu, S., Yu, K.: Neural graph matching networks for Chinese short text matching. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6152\u20136158 (2020)"},{"key":"1037_CR24","doi-asserted-by":"crossref","unstructured":"Lyu Lu, B., Su, C., Yu, Z.K.: Let: Linguistic knowledge enhanced graph transformer for Chinese short text matching. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp. 13498\u201313506 (2021)","DOI":"10.1609\/aaai.v35i15.17592"},{"key":"1037_CR25","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301 (2013)"},{"key":"1037_CR26","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Zhang, C., Xia, C., Philip, S.Y.: Semantic matching and aggregation network for few-shot intent detection. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing: Findings, pp. 1209\u20131218 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.108"},{"key":"1037_CR27","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.knosys.2018.02.034","volume":"148","author":"H Peng","year":"2018","unstructured":"Peng, H., Ma, Y., Li, Y., Cambria, E.: Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl.-Based Syst. 148, 167\u2013176 (2018)","journal-title":"Knowl.-Based Syst."},{"key":"1037_CR28","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv: 1910.01108 (2019)"},{"key":"1037_CR29","unstructured":"Su, J.: Text emotion classification (iv): Better loss function (2017).\u00a0https:\/\/spaces.ac.cn\/archives\/4293. Accessed 30 March 2017"},{"key":"1037_CR30","doi-asserted-by":"crossref","unstructured":"Tan, C., Wei, F., Wang, W., Lv, W., Zhou, M.: Multiway attention networks for modeling sentence pairs. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 4411\u20134417 (2018)","DOI":"10.24963\/ijcai.2018\/613"},{"key":"1037_CR31","doi-asserted-by":"crossref","unstructured":"Tao, H., Tong, S., Zhang, K., Xu, T., Liu, Q., Chen, E., Hou, M.: Ideography leads us to the field of cognition: A radical-guided associative model for Chinese text classification. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp. 13898\u201313906 (2021)","DOI":"10.1609\/aaai.v35i15.17637"},{"key":"1037_CR32","doi-asserted-by":"crossref","unstructured":"Tao, H., Tong, S., Zhao, H., Xu, T., Jin, B., Liu, Q.: A radical-aware attention-based model for Chinese text classification. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 5125\u20135132 (2019)","DOI":"10.1609\/aaai.v33i01.33015125"},{"key":"1037_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 4144\u20134150 (2017)","DOI":"10.24963\/ijcai.2017\/579"},{"key":"1037_CR34","first-page":"1","volume":"20","author":"H Wang","year":"2021","unstructured":"Wang, H., Wang, B., Duan, J., Zhang, J.: Chinese spelling error detection using a fusion lattice LSTM. ACM Trans. Asian Low Resour Lang. Inf. Process. 20, 1\u201311 (2021)","journal-title":"ACM Trans. Asian Low Resour Lang. Inf. Process."},{"issue":"9","key":"1037_CR35","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.3390\/math8091558","volume":"8","author":"L Xiang","year":"2020","unstructured":"Xiang, L., Yang, S., Liu, Y., Li, Q., Zhu, C.: Novel linguistic steganography based on character-level text generation. Mathematics 8(9), 1558 (2020)","journal-title":"Mathematics"},{"key":"1037_CR36","unstructured":"Xing, X., Wang, T., Yang, Y, Hanjalic, A., Shen, H.T.: Radial graph convolutional network for visual question generation. IEEE Transactions on Neural Networks and Learning Systems (2020)"},{"key":"1037_CR37","doi-asserted-by":"publisher","first-page":"601","DOI":"10.32604\/cmc.2019.05691","volume":"61","author":"Z Xu","year":"2019","unstructured":"Xu, Z., Lu, W., Li, F., Peng, X., Zhang, R.: Deep feature fusion model for sentence semantic matching. Computers, Materials and Continua 61, 601\u2013616 (2019)","journal-title":"Computers, Materials and Continua"},{"key":"1037_CR38","doi-asserted-by":"crossref","unstructured":"Xu, X., Wang, T., Yang, Y., Zuo, L., Shen, F., Shen, H.T.: Cross-modal attention with semantic consistence for image-text matching. IEEE Transactions on Neural Networks and Learning Systems (2020)","DOI":"10.1109\/TNNLS.2020.2967597"},{"key":"1037_CR39","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1007\/s13042-021-01349-x","volume":"12","author":"R Yu","year":"2021","unstructured":"Yu, R., Lu, W., Lu, H., Wang, S., Li, F., Zhang, X., Yu, J.: Sentence pair modeling based on semantic feature map for human interaction with IoT devices. Int. J. Mach. Learn. Cybern. 12, 3081\u20133099 (2021)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"1037_CR40","doi-asserted-by":"crossref","unstructured":"Yu, S., Wang, S., Li, Y., Feng, S., Tian, H., Wu, H., Wang, H.: Ernie 2.0: A continual pre-training framework for language understanding. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 8968\u20138975 (2020)","DOI":"10.1609\/aaai.v34i05.6428"},{"key":"1037_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, K., Lv, G., Wang, L., Wu, L., Chen, E., Wu, F., Xie, X.: Drr-net: Dynamic re-read network for sentence semantic matching. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 7442\u20137449 (2019)","DOI":"10.1609\/aaai.v33i01.33017442"},{"key":"1037_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, K., Wu, L., Lv, G., Wang, M., Chen, E., Ruan, S.: Making the relation matters: Relation of relation learning network for sentence semantic matching. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp. 14411\u201314419 (2021)","DOI":"10.1609\/aaai.v35i16.17694"},{"key":"1037_CR43","doi-asserted-by":"crossref","unstructured":"Zhao, S., Huang, Y., Su, C., Li, Y., Wang, F.: Interactive attention networks for semantic text matching. In: Proceedings of the IEEE International Conference on Data Mining, pp. 861\u2013870 (2020)","DOI":"10.1109\/ICDM50108.2020.00095"},{"key":"1037_CR44","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, W., Li, Y., Yu, J., Jian, P., Zhang, X.: Chinese semantic matching with multi-granularity alignment and feature fusion. In: Proceedings of the International Joint Conference on Neural Networks (2021)","DOI":"10.1109\/IJCNN52387.2021.9534130"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-022-01037-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-022-01037-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-022-01037-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T21:25:03Z","timestamp":1675459503000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-022-01037-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,28]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["1037"],"URL":"https:\/\/doi.org\/10.1007\/s11280-022-01037-y","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,28]]},"assertion":[{"value":"31 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2022","order":4,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}