{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T06:28:53Z","timestamp":1774074533376,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Innovation Foundation of Science and Technology of Dalian","award":["2018J12GX045"],"award-info":[{"award-number":["2018J12GX045"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018AAA0100300"],"award-info":[{"award-number":["2018AAA0100300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s10489-021-02492-2","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T01:02:32Z","timestamp":1623805352000},"page":"2530-2538","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Self attention mechanism of bidirectional information enhancement"],"prefix":"10.1007","volume":"52","author":[{"given":"Qibin","family":"Li","sequence":"first","affiliation":[]},{"given":"Nianmin","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yanan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"2492_CR1","doi-asserted-by":"crossref","unstructured":"Williams A, Nangia N, Bowman SR (2018) A broad-coverage challenge corpus for sentence understanding through inference. In: NAACL","DOI":"10.18653\/v1\/N18-1101"},{"key":"2492_CR2","doi-asserted-by":"crossref","unstructured":"Warstadt A, Singh A, Bowman SR (2018) Neural network acceptability judgments. arXiv preprint arXiv:1805.12471","DOI":"10.1162\/tacl_a_00290"},{"key":"2492_CR3","doi-asserted-by":"crossref","unstructured":"Wang A, Singh A, Michael J, Hill F, Levy O, Bowman S (2018) Glue: a multi-task benchmark and analysis platform for natural language understanding. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and interpreting neural networks for NLP, pp 353\u2013355","DOI":"10.18653\/v1\/W18-5446"},{"key":"2492_CR4","unstructured":"Vaswani A, Shazeer N, Parmar N, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I Attention is all you need. Advances in Neural Information Processing Systems 30 (NIPS 2017), pp 6000\u20136010"},{"key":"2492_CR5","unstructured":"Dos Santos C, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 69\u201378"},{"key":"2492_CR6","unstructured":"dos Santos C, Tan M, Xiang B, Zhou B (2016) Attentive pooling networks. arXiv preprint arXiv:1602.03609"},{"key":"2492_CR7","unstructured":"Zhang D, Wang D (2015) Relation classification via recurrent neural network. In: Proceedings of CoRR, arXiv:1508.01006"},{"key":"2492_CR8","doi-asserted-by":"crossref","unstructured":"Correia GM, Niculae V, Martins AFT (2019) Adaptively sparse transformers. In: Proceedings of the 2019 conference on empirical methods in natural language processing and 9th international joint conference on natural language processing (EMNLP-IJCNLP)","DOI":"10.18653\/v1\/D19-1223"},{"key":"2492_CR9","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: proceedings of the 2015 conference on international conference on learning representations(ICLR)"},{"key":"2492_CR10","unstructured":"Zeng D, Liu K, Lai S, Zhou G, Zhao J (2014) Relation classification via convolutional deep neural network. In: Proceedings of International Conference on Computational Linguistics (COLING)"},{"key":"2492_CR11","doi-asserted-by":"crossref","unstructured":"Hill F, Cho K, Korhonen A (2016) Learning distributed representations of sentences from unlabelled data. In: Proceedings of the conference of the North American chapter of the association for computational linguistics: human language technologies . Association for Computational Linguistics, San Diego, California, pp 1367\u20131377","DOI":"10.18653\/v1\/N16-1162"},{"key":"2492_CR12","unstructured":"Xu H, Gao T, Yao Y, Ye D, Liu Z, Maosong S (2019) In: Proceedings of EMNLP-IJCNLP, System Demonstrations"},{"key":"2492_CR13","doi-asserted-by":"crossref","unstructured":"Hendrickx I, Kim SN, Kozareva Z, Nakov P, S\u00e9aghdha D\u00d3, Pad\u00f3 S, Pennacchiotti M, Romano L, Szpakowicz S (2009) Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In: proceedings of the Workshop on SemEval","DOI":"10.3115\/1621969.1621986"},{"key":"2492_CR14","unstructured":"Bilan I, Roth B (2018) Position-aware self-attention with relative positional encodings for slot filling. In: Proceedings of CoRR, arXiv:1807.03052"},{"key":"2492_CR15","doi-asserted-by":"crossref","unstructured":"Du J, Han J, Way A, Wan D (2018) Multi-level structured self-attentions for distantly supervised relation extraction. In: Proceedings of the 2018 conference on empirical methods in natural language processing(EMNLP)","DOI":"10.18653\/v1\/D18-1245"},{"key":"2492_CR16","doi-asserted-by":"crossref","unstructured":"Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of annual meeting of the association for computational linguistics (ACL), pp 1556\u20131566","DOI":"10.3115\/v1\/P15-1150"},{"key":"2492_CR17","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell, Neural image caption generation with visual attention. In: Proceedings of international conference on machine learning (ICML)"},{"key":"2492_CR18","doi-asserted-by":"crossref","unstructured":"Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of SIGMOD","DOI":"10.1145\/1376616.1376746"},{"key":"2492_CR19","doi-asserted-by":"crossref","unstructured":"Mou L, Peng H, Ge L, Yan X, Zhang L, Jin Z (2015) Discriminative neural sentence modeling by tree-based convolution. In: Proceedings of the 2015 conference on empirical methods in natural language processing(EMNLP), pp 2315\u20132325","DOI":"10.18653\/v1\/D15-1279"},{"key":"2492_CR20","doi-asserted-by":"crossref","unstructured":"Wang L, Cao Z, de Melo G, Liu Z (2016) Relation classification via multi-level attention cnns. In: Proceedings of the 54th annual meeting of the association for computational linguistics, Association for Computational Linguistics, pp 1298\u20131307","DOI":"10.18653\/v1\/P16-1123"},{"key":"2492_CR21","unstructured":"Soares LB, FitzGerald N, Ling J, Kwiatkowski T (2019) Matching the blanks, Distributional similarity for relation learning. In: Proceedings of ACL"},{"key":"2492_CR22","unstructured":"Bentivogli L, Magnini B, Dagan I, Dang HT, Giampiccolo D (2009) The fifth PASCAL recognizing textual entailment challenge. In: TAC. NIST"},{"key":"2492_CR23","doi-asserted-by":"crossref","unstructured":"Mintz M, Bills S, Snow R, Dan J (2009) Distant Supervision for relation extraction without labeled data. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: pp 2,1003\u20131011","DOI":"10.3115\/1690219.1690287"},{"key":"2492_CR24","doi-asserted-by":"crossref","unstructured":"Ma M, Huang L, Xiang B, Zhou B (2015) Dependency-based convolutional neural networks for sentence embedding. In: proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, vol 2, pp 174\u2013 179","DOI":"10.3115\/v1\/P15-2029"},{"key":"2492_CR25","doi-asserted-by":"crossref","unstructured":"Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network formodelling sentences. arXiv preprint arXiv:1404.2188","DOI":"10.3115\/v1\/P14-1062"},{"key":"2492_CR26","doi-asserted-by":"crossref","unstructured":"Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) Attentionbased bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th annual meeting of the association for computational linguistics,2, pp 207\u2013212","DOI":"10.18653\/v1\/P16-2034"},{"key":"2492_CR27","doi-asserted-by":"crossref","unstructured":"Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) Squad: 100 000+ questions for machine comprehension of text. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 2383\u20132392","DOI":"10.18653\/v1\/D16-1264"},{"key":"2492_CR28","unstructured":"Le QV, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of international conference on machine learning (ICML), vol 14, pp 1188\u20131196"},{"key":"2492_CR29","unstructured":"Hoffmann R, Zhang C, Ling X, Zettlemoyer L, Weld DS (2011) Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th ACL-HLT, pp 541\u2013550"},{"key":"2492_CR30","unstructured":"Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng A, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631\u20131642"},{"key":"2492_CR31","unstructured":"Kiros R, Zhu Y, Salakhutdinov RR, Zemel R, Urtasun R, Torralba A, Fidler S (2015) Skip-thought vectors. In: Advances in neural information processing systems, pp 3294\u20133302"},{"key":"2492_CR32","doi-asserted-by":"crossref","unstructured":"Sukhbaatar S, Grave E, Bojanowski P, Joulin A (2019) Adaptive attention span in transformers. In: Proc ACL","DOI":"10.18653\/v1\/P19-1032"},{"key":"2492_CR33","doi-asserted-by":"crossref","unstructured":"Riedel S, Yao L, Andrew M (2010) Modeling relations and their mentions without labeled text. In: Proceedings of the 2010 European conference on machine learning and knowledge discovery in databases, Part III, pp 148\u2013163","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"2492_CR34","unstructured":"Tao S, Tianyi Z, Guodong L, Jing J, Sen W, Chengqi Z (2018) Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling. arXiv preprint arXiv:1801.10296"},{"key":"2492_CR35","unstructured":"Zhang S, Zheng D, Hu X, Yang M (2015) Bidirectional long short-term memory networks for relation classification. In: Proceedings of PACLIC"},{"key":"2492_CR36","doi-asserted-by":"crossref","unstructured":"Vashishth S, Joshi R, Prayaga SS, Bhattacharyya C, Talukdar P (2018) Improving distantly-supervised neural relation extraction using side information. In: Proceedings of the 2018 conference on empirical methods in natural language processing","DOI":"10.18653\/v1\/D18-1157"},{"key":"2492_CR37","doi-asserted-by":"crossref","unstructured":"Lin Y, Shen S, Liu Z, Luan H, Sun M (2016) Neural relation extraction with selective attention over instances. In: Proceedings of the 54th ACL","DOI":"10.18653\/v1\/P16-1200"},{"key":"2492_CR38","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Sun M (2017) Neural relation extraction with multi-lingual attention. In: Proceedings of the 55th ACL, pp 34\u201343","DOI":"10.18653\/v1\/P17-1004"},{"key":"2492_CR39","unstructured":"Ye Z-X, Ling Z-H (2019) Improving distantly-supervised neural relation extraction using side information. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics, pp 2810\u20132819"},{"key":"2492_CR40","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: Inproceedings of the conference on empirical methods in natural language processing (EMNLP).ngzhuyi","DOI":"10.3115\/v1\/D14-1181"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02492-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02492-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02492-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T05:13:49Z","timestamp":1644470029000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02492-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,16]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["2492"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02492-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,16]]},"assertion":[{"value":"30 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}