{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T08:44:42Z","timestamp":1751013882521},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T00:00:00Z","timestamp":1618531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T00:00:00Z","timestamp":1618531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973238"],"award-info":[{"award-number":["61973238"]}],"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":["61773292"],"award-info":[{"award-number":["61773292"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["19DZ1209200"],"award-info":[{"award-number":["19DZ1209200"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s10489-021-02269-7","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T05:43:43Z","timestamp":1618551823000},"page":"8928-8944","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A two-domain coordinated sentence similarity scheme for question-answering robots regarding unpredictable outliers and non-orthogonal categories"],"prefix":"10.1007","volume":"51","author":[{"given":"Boyang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weisheng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"key":"2269_CR1","doi-asserted-by":"crossref","unstructured":"Almansor EH, Hussain FK (2019) Survey on intelligent chatbots: State-of-the-art and future research directions. In: Barolli L, Hussain FK, Ikeda M (eds) Complex, intelligent, and software intensive systems - Proceedings of the 13th international conference on complex, intelligent, and software intensive systems, CISIS 2019, Sydney, NSW, Australia, 3-5 July 2019, of advances in intelligent systems and computing, vol 993. Springer, pp 534\u2013543","DOI":"10.1007\/978-3-030-22354-0_47"},{"key":"2269_CR2","doi-asserted-by":"crossref","unstructured":"Andor D, He L, Lee K, Pitler E (2019) Giving BERT a calculator: Finding operations and arguments with reading comprehension. In: Inui K, Jiang J, Ng V, Wan X (eds) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, association for computational linguistics, pp 5946\u20135951","DOI":"10.18653\/v1\/D19-1609"},{"key":"2269_CR3","doi-asserted-by":"crossref","unstructured":"Beltagy I, Lo K, Cohan A (2019) Scibert: A pretrained language model for scientific text. In: Inui K, Jiang J, Ng V, Wan X (eds) 3613\u20133618.","DOI":"10.18653\/v1\/D19-1371"},{"key":"2269_CR4","unstructured":"Bird S, Dale R, Dorr BJ, Gibson BR, Joseph MT, Kan M-Y, Lee D, Powley B, Radev DR, Tan YF (2008) The ACL anthology reference corpus: A reference dataset for bibliographic research in computational linguistics. In: Proceedings of the International Conference on Language Resources and Evaluation, LREC 2008, 26 May - 1 June 2008, Marrakech, Morocco. European Language Resources Association"},{"key":"2269_CR5","doi-asserted-by":"crossref","unstructured":"Chen D, Fisch A, Weston J, Bordes A (2017) Reading wikipedia to answer open-domain questions. In: Barzilay R, Kan M-Y (eds) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long papers, Association for computational linguistics, pp 1870\u20131879","DOI":"10.18653\/v1\/P17-1171"},{"key":"2269_CR6","unstructured":"Choromanski K, Likhosherstov V, Dohan D, Song X, Gane A, Sarl\u00f3s T, Hawkins P, Davis J, Mohiuddin A, Kaiser L, Belanger D, Colwell L, Weller A (2020) Rethinking attention with performers. arXiv:2009.14794"},{"key":"2269_CR7","doi-asserted-by":"crossref","unstructured":"Clark K, Khandelwal U, Levy O, Manning CD (2019) What does BERT look at? an analysis of bert\u2019s attention. arXiv:1906.04341","DOI":"10.18653\/v1\/W19-4828"},{"key":"2269_CR8","unstructured":"Clark K, Luong M-T, Le QV, Manning CD (2020) ELECTRA: Pre-training text encoders as discriminators rather than generators. In: 8th international conference on learning representations, ICLR 2020, addis ababa, ethiopia, april 26-30, 2020. Openreview.net"},{"key":"2269_CR9","doi-asserted-by":"crossref","unstructured":"Cohan A, Ammar W, Van Zuylen M, Cady F (2019) Structural scaffolds for citation intent classification in scientific publications. In: NAACL","DOI":"10.18653\/v1\/N19-1361"},{"key":"2269_CR10","doi-asserted-by":"crossref","unstructured":"Cui Y, Che W, Liu T, Qin B, Wang S, Hu G (2020) Revisiting pre-trained models for Chinese natural language processing. In: Proceedings of the 2020 conference on empirical methods in natural language processing: findings, Online, November association for computational linguistics, pp 657\u2013668","DOI":"10.18653\/v1\/2020.findings-emnlp.58"},{"key":"2269_CR11","unstructured":"Cui Y, Che W, Liu T, Qin B, Yang Z, Wang S, Guoping H (2019) Pre-training with whole word masking for chinese BERT. arXiv:1906.08101"},{"key":"2269_CR12","doi-asserted-by":"crossref","unstructured":"Evseev DA, Yu Arkhipov M (2020) Sparql query generation for complex question answering with bert and bilstm-based model. In: International Conference on Computational Linguistics and Intellectual Technologies \u201cDialogue\u201d","DOI":"10.28995\/2075-7182-2020-19-270-282"},{"key":"2269_CR13","unstructured":"Dai Z, Lai G, Yang Y, Le Q (2020) Funnel-transformer: Filtering out sequential redundancy for efficient language processing. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin HT (eds) Advances in neural information processing systems 33: Annual conference on neural information processing systems 2020, NeurIPS 2020, December 6-12, 2020, virtual"},{"key":"2269_CR14","unstructured":"Dernoncourt F, Young Lee J (2017) Pubmed 200k RCT: A dataset for sequential sentence classification in medical abstracts. In: Kondrak G, Watanabe T (eds) Proceedings of the eighth international joint conference on natural language processing, IJCNLP 2017, Taipei, Taiwan, November 27 - December 1, 2017, Short Papers,. Asian Federation of natural language processing, vol 2, pp 308\u2013313"},{"key":"2269_CR15","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, (Long and Short Papers) association for computational linguistics, vol 1, pp 4171\u20134186"},{"key":"2269_CR16","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27: Annual conference on neural information processing systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp 2672\u20132680"},{"key":"2269_CR17","unstructured":"He P, Liu X, Gao J, Chen W (2020) Deberta: Decoding-enhanced BERT with disentangled attention. arXiv:2006.03654"},{"key":"2269_CR18","doi-asserted-by":"crossref","unstructured":"Hu D (2019) An introductory survey on attention mechanisms in NLP problems. In: Bi Y, Bhatia R, Kapoor S (eds) Intelligent systems and applications - Proceedings of the 2019 intelligent systems conference, IntelliSys 2019, London, UK, September 5-6, 2019, Volume 2, of advances in intelligent systems and computing, vol 432\u2013448. Springer, p 1038","DOI":"10.1007\/978-3-030-29513-4_31"},{"key":"2269_CR19","doi-asserted-by":"crossref","unstructured":"Jin D, Szolovits P (2018) Hierarchical neural networks for sequential sentence classification in medical scientific abstracts. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds) Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31 - November 4, 2018, association for computational linguistics, pp 3100\u20133109","DOI":"10.18653\/v1\/D18-1349"},{"key":"2269_CR20","unstructured":"Karpukhin V, Oguz B, Min S, Lewis PSH, Ledell W, Edunov S, Chen D, Yih W-T (2020) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 Online, November 16-20, 2020 association for computational linguistics. In: Webber B, Cohn T, He Y, Liu Y (eds), pp 6769\u20136781"},{"key":"2269_CR21","unstructured":"Kitaev N, Kaiser L, Levskaya A (2020) Reformer: The efficient transformer. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net"},{"key":"2269_CR22","doi-asserted-by":"crossref","unstructured":"Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: Knight K, Nenkova A, Rambow O (eds) NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational linguistics: Human Language Technologies, San Diego California, USA, June 12-17, 2016, The Association for Computational Linguistics, pp 260\u2013270","DOI":"10.18653\/v1\/N16-1030"},{"key":"2269_CR23","unstructured":"Liu Y, Ott M, Goyal N, Jingfei D, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized BERT pretraining approach. arXiv:1907.11692"},{"key":"2269_CR24","unstructured":"Neculoiu P, Versteegh M, Rotaru M (2016) Proceedings of the 1st Workshop on Representation Learning for NLP, rep4NLP@ACL 2016, Berlin, Germany, August 11, 2016 Association for Computational Linguistics. In: Blunsom P, Cho K, Cohen SB, Grefenstette E, Hermann KM, Rimell L, Weston J, Yih SW-T (eds), pp 148\u2013157"},{"key":"2269_CR25","doi-asserted-by":"crossref","unstructured":"Papineni Kishore, Roukos Salim, Ward Todd, Zhu W-J (2002) Bleu: A method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics, July 6-12, 2002. ACL, Philadelphia, pp 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"2269_CR26","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. In: Inui K, Jiang J, Ng V, Wan X (eds) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, association for computational linguistics, pp 3980\u20133990","DOI":"10.18653\/v1\/D19-1410"},{"key":"2269_CR27","unstructured":"Sorokin D, Gurevych I (2018) Modeling semantics with gated graph neural networks for knowledge base question answering. In: Bender EM, Derczynski L, Isabelle P (eds) Proceedings of the 27th international conference on computational linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20-26, 2018, association for computational linguistics, pp 3306\u20133317"},{"issue":"8","key":"2269_CR28","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.1007\/s10489-020-01680-w","volume":"50","author":"T Wang","year":"2020","unstructured":"Wang T, Liu L, Liu N, Zhang H, Zhang L, Feng S (2020) A multi-label text classification method via dynamic semantic representation model and deep neural network. Appl Intell 50(8):2339\u20132351","journal-title":"Appl Intell"},{"key":"2269_CR29","doi-asserted-by":"crossref","unstructured":"Wei JW, Zou K (2019) EDA: easy data augmentation techniques for boosting performance on text classification tasks. In: Inui K, Jiang J, Ng V, Wan X (eds) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, association for computational linguistics, pp 6381\u20136387","DOI":"10.18653\/v1\/D19-1670"},{"key":"2269_CR30","unstructured":"Xu N (2003) Chinese word segmentation as character tagging Int J Comput Linguistics Chin Lang Process 8(1)"},{"key":"2269_CR31","doi-asserted-by":"crossref","unstructured":"Yamada I, Asai A, Shindo H, Takeda H, Matsumoto Y (2020) LUKE: Deep contextualized entity representations with entity-aware self-attention. In: Webber B, Cohn T, He Y, Liu Y (eds) Proceedings of the 2020 conference on empirical methods in natural language processing, EMNLP 2020, Online, November 16-20, 2020 Association for Computational Linguistics, pp 6442\u20136454","DOI":"10.18653\/v1\/2020.emnlp-main.523"},{"key":"2269_CR32","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell JG, Salakhutdinov R, Le QV (2019) Xlnet: Generalized autoregressive pretraining for language understanding. In: wallach HM, Larochelle H, Beygelzimer A, d\u2019Alch\u0117-Buc F, Fox EB, Garnett R (eds) Advances in neural information processing systems 32: Annual conference on neural information processing systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp 5754\u20135764"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02269-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02269-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02269-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T05:13:09Z","timestamp":1636434789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02269-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,16]]},"references-count":32,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["2269"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02269-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,16]]},"assertion":[{"value":"9 February 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}