{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T18:16:04Z","timestamp":1773944164384,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2020YFC2003502"],"award-info":[{"award-number":["No.2020YFC2003502"]}],"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":["No.62276038"],"award-info":[{"award-number":["No.62276038"]}],"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":["No.62221005"],"award-info":[{"award-number":["No.62221005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation for Innovative Research Groups of Natural Science Foundation of Chongqing","award":["No.cstc2019jcyjcxttX0002"],"award-info":[{"award-number":["No.cstc2019jcyjcxttX0002"]}]},{"name":"ey Cooperation Project of Chongqing Municipal Education Commission","award":["HZ2021008"],"award-info":[{"award-number":["HZ2021008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10489-023-05111-4","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T02:01:48Z","timestamp":1698976908000},"page":"29656-29676","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Joint relational triple extraction based on potential relation detection and conditional entity mapping"],"prefix":"10.1007","volume":"53","author":[{"given":"Xiong","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6154-4656","authenticated-orcid":false,"given":"Qinghua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,3]]},"reference":[{"key":"5111_CR1","doi-asserted-by":"publisher","unstructured":"Yao X, Van\u00a0Durme B (2014) Information extraction over structured data: Question answering with Freebase. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 956\u2013966. https:\/\/doi.org\/10.3115\/v1\/P14-1090","DOI":"10.3115\/v1\/P14-1090"},{"key":"5111_CR2","doi-asserted-by":"publisher","unstructured":"Miwa M, Bansal M (2016) End-to-end relation extraction using LSTMs on sequences and tree structures. In: Proceedings of the 54th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 1105\u20131116. https:\/\/doi.org\/10.18653\/v1\/P16-1105","DOI":"10.18653\/v1\/P16-1105"},{"key":"5111_CR3","doi-asserted-by":"publisher","unstructured":"Li Q, Ji H (2014) Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 402\u2013412. https:\/\/doi.org\/10.3115\/v1\/P14-1038","DOI":"10.3115\/v1\/P14-1038"},{"key":"5111_CR4","doi-asserted-by":"publisher","unstructured":"Li X, Li Y, Yang J, et\u00a0al (2022) A relation aware embedding mechanism for relation extraction. Appl Intell 52(9):10,022\u201310,031. https:\/\/doi.org\/10.1007\/s10489-021-02699-3","DOI":"10.1007\/s10489-021-02699-3"},{"key":"5111_CR5","doi-asserted-by":"publisher","unstructured":"Luan Y, He L, Ostendorf M, et\u00a0al (2018) Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3219\u20133232. https:\/\/doi.org\/10.18653\/v1\/D18-1360","DOI":"10.18653\/v1\/D18-1360"},{"key":"5111_CR6","doi-asserted-by":"publisher","unstructured":"Lin Y, Liu Z, Sun M, et\u00a0al (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence, pp 2181\u20132187. https:\/\/doi.org\/10.1609\/aaai.v29i1.9491","DOI":"10.1609\/aaai.v29i1.9491"},{"issue":"1","key":"5111_CR7","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1186\/s12859-017-1609-9","volume":"18","author":"F Li","year":"2017","unstructured":"Li F, Zhang M, Fu G et al (2017) A neural joint model for entity and relation extraction from biomedical text. BMC Bioinformatics 18(1):198. https:\/\/doi.org\/10.1186\/s12859-017-1609-9","journal-title":"BMC Bioinformatics"},{"key":"5111_CR8","doi-asserted-by":"publisher","unstructured":"Lin Y, Shen S, Liu Z, et\u00a0al (2016) Neural relation extraction with selective attention over instances. In: Proceedings of the 54th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 2124\u20132133. https:\/\/doi.org\/10.18653\/v1\/P16-1200","DOI":"10.18653\/v1\/P16-1200"},{"key":"5111_CR9","doi-asserted-by":"publisher","unstructured":"Zhong Z, Chen D (2021) A frustratingly easy approach for entity and relation extraction. In: Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 50\u201361. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.5","DOI":"10.18653\/v1\/2021.naacl-main.5"},{"key":"5111_CR10","doi-asserted-by":"publisher","unstructured":"Ye D, Lin Y, Li P, et\u00a0al (2022) Packed levitated marker for entity and relation extraction. In: Proceedings of the 60th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 4904\u20134917. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.337","DOI":"10.18653\/v1\/2022.acl-long.337"},{"key":"5111_CR11","doi-asserted-by":"publisher","unstructured":"Ren X, Wu Z, He W, et\u00a0al (2017) Cotype: Joint extraction of typed entities and relations with knowledge bases. In: Proceedings of the 26th international conference on world wide web, pp 1015\u20131024. https:\/\/doi.org\/10.1145\/3038912.3052708","DOI":"10.1145\/3038912.3052708"},{"key":"5111_CR12","doi-asserted-by":"publisher","unstructured":"Wei Z, Su J, Wang Y, et\u00a0al (2020) A novel cascade binary tagging framework for relational triple extraction. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 1476\u20131488. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.136","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"5111_CR13","doi-asserted-by":"publisher","unstructured":"Li X, Luo X, Dong C, et\u00a0al (2021) TDEER: An efficient translating decoding schema for joint extraction of entities and relations. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 8055\u20138064. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.635","DOI":"10.18653\/v1\/2021.emnlp-main.635"},{"key":"5111_CR14","doi-asserted-by":"publisher","unstructured":"Yu B, Zhang Z, Shu X, et\u00a0al (2020) Joint extraction of entities and relations based on a novel decomposition strategy. In: 24th European conference on artificial intelligence and 10th conference on prestigious applications of artificial intelligence, pp 2282\u20132289. https:\/\/doi.org\/10.3233\/FAIA200356","DOI":"10.3233\/FAIA200356"},{"key":"5111_CR15","doi-asserted-by":"publisher","unstructured":"Zeng X, Zeng D, He S, et\u00a0al (2018) Extracting relational facts by an end-to-end neural model with copy mechanism. In: Proceedings of the 56th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 506\u2013514. https:\/\/doi.org\/10.18653\/v1\/P18-1047","DOI":"10.18653\/v1\/P18-1047"},{"key":"5111_CR16","doi-asserted-by":"publisher","unstructured":"Zeng D, Zhang H, Liu Q (2020) Copymtl: Copy mechanism for joint extraction of entities and relations with multi-task learning. In: The thirty-fourth AAAI conference on artificial intelligence, pp 9507\u20139514. https:\/\/doi.org\/10.1609\/aaai.v34i05.6495","DOI":"10.1609\/aaai.v34i05.6495"},{"key":"5111_CR17","doi-asserted-by":"publisher","unstructured":"Eberts M, Ulges A (2020) Span-based joint entity and relation extraction with transformer pre-training. In: 24th European conference on artificial intelligence and 10th conference on prestigious applications of artificial intelligence, pp 2006\u20132013. https:\/\/doi.org\/10.3233\/FAIA200321","DOI":"10.3233\/FAIA200321"},{"key":"5111_CR18","doi-asserted-by":"publisher","unstructured":"Ji B, Yu J, Li S, et\u00a0al (2020) Span-based joint entity and relation extraction with attention-based span-specific and contextual semantic representations. In: Proceedings of the 28th international conference on computational linguistics, pp 88\u201399. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.8","DOI":"10.18653\/v1\/2020.coling-main.8"},{"key":"5111_CR19","doi-asserted-by":"publisher","unstructured":"Zhao T, Yan Z, Cao Y, et\u00a0al (2020) Asking effective and diverse questions: A machine reading comprehension based framework for joint entity-relation extraction. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence, pp 3948\u20133954. https:\/\/doi.org\/10.24963\/ijcai.2020\/546","DOI":"10.24963\/ijcai.2020\/546"},{"key":"5111_CR20","doi-asserted-by":"publisher","unstructured":"Li X, Yin F, Sun Z, et\u00a0al (2019) Entity-relation extraction as multi-turn question answering. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1340\u20131350. https:\/\/doi.org\/10.18653\/v1\/P19-1129","DOI":"10.18653\/v1\/P19-1129"},{"key":"5111_CR21","doi-asserted-by":"publisher","unstructured":"Zheng H, Wen R, Chen X, et\u00a0al (2021) PRGC: Potential relation and global correspondence based joint relational triple extraction. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (vol 1: Long Papers), pp 6225\u20136235. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.486","DOI":"10.18653\/v1\/2021.acl-long.486"},{"key":"5111_CR22","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang MW, Lee K, et\u00a0al (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, vol 1 (Long and Short Papers), pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"5111_CR23","doi-asserted-by":"publisher","unstructured":"Wang Y, Yu B, Zhang Y, et\u00a0al (2020) TPLinker: Single-stage joint extraction of entities and relations through token pair linking. In: Proceedings of the 28th international conference on computational linguistics, pp 1572\u20131582. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.138","DOI":"10.18653\/v1\/2020.coling-main.138"},{"key":"5111_CR24","doi-asserted-by":"crossref","unstructured":"Mintz M, Bills S, Snow R, et\u00a0al (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 1003\u20131011. https:\/\/aclanthology.org\/P09-1113","DOI":"10.3115\/1690219.1690287"},{"key":"5111_CR25","doi-asserted-by":"publisher","unstructured":"Akbik A, Bergmann T, Vollgraf R (2019) Pooled contextualized embeddings for named entity recognition. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, vol 1 (Long and Short Papers), pp 724\u2013728. https:\/\/doi.org\/10.18653\/v1\/N19-1078","DOI":"10.18653\/v1\/N19-1078"},{"key":"5111_CR26","doi-asserted-by":"publisher","unstructured":"Luo Y, Xiao F, Zhao H (2020) Hierarchical contextualized representation for named entity recognition. In: The thirty-fourth AAAI conference on artificial intelligence, pp 8441\u20138448. https:\/\/doi.org\/10.1609\/aaai.v34i05.6363","DOI":"10.1609\/aaai.v34i05.6363"},{"key":"5111_CR27","unstructured":"Zeng D, Liu K, Lai S, et\u00a0al (2014) Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 2335\u20132344. https:\/\/aclanthology.org\/C14-1220"},{"key":"5111_CR28","doi-asserted-by":"publisher","unstructured":"Zhou P, Shi W, Tian J, et\u00a0al (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th annual meeting of the association for computational linguistics (vol 2: Short Papers), pp 207\u2013212. https:\/\/doi.org\/10.18653\/v1\/P16-2034","DOI":"10.18653\/v1\/P16-2034"},{"key":"5111_CR29","doi-asserted-by":"publisher","unstructured":"Miwa M, Sasaki Y (2014) Modeling joint entity and relation extraction with table representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1858\u20131869. https:\/\/doi.org\/10.3115\/v1\/D14-1200","DOI":"10.3115\/v1\/D14-1200"},{"key":"5111_CR30","doi-asserted-by":"publisher","unstructured":"Yuan Y, Zhou X, Pan S, et\u00a0al (2020) A relation-specific attention network for joint entity and relation extraction. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence, pp 4054\u20134060. https:\/\/doi.org\/10.24963\/ijcai.2020\/561","DOI":"10.24963\/ijcai.2020\/561"},{"key":"5111_CR31","doi-asserted-by":"publisher","first-page":"137","DOI":"10.3233\/SHTI220418","volume":"294","author":"B Pfeifer","year":"2022","unstructured":"Pfeifer B, Holzinger A, Schimek MG (2022) Robust random forest-based all-relevant feature ranks for trustworthy ai. Stud Health Technol Inform 294:137\u2013138. https:\/\/doi.org\/10.3233\/SHTI220418","journal-title":"Stud Health Technol Inform"},{"key":"5111_CR32","doi-asserted-by":"publisher","unstructured":"Zheng S, Wang F, Bao H, et\u00a0al (2017) Joint extraction of entities and relations based on a novel tagging scheme. In: Proceedings of the 55th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 1227\u20131236. https:\/\/doi.org\/10.18653\/v1\/P17-1113","DOI":"10.18653\/v1\/P17-1113"},{"key":"5111_CR33","doi-asserted-by":"publisher","unstructured":"Dixit K, Al-Onaizan Y (2019) Span-level model for relation extraction. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5308\u20135314. https:\/\/doi.org\/10.18653\/v1\/P19-1525","DOI":"10.18653\/v1\/P19-1525"},{"key":"5111_CR34","doi-asserted-by":"publisher","unstructured":"Fu TJ, Li PH, Ma WY (2019) GraphRel: Modeling text as relational graphs for joint entity and relation extraction. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1409\u20131418. https:\/\/doi.org\/10.18653\/v1\/P19-1136","DOI":"10.18653\/v1\/P19-1136"},{"issue":"107","key":"5111_CR35","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.knosys.2021.107298","volume":"228","author":"Q Wan","year":"2021","unstructured":"Wan Q, Wei L, Chen X et al (2021) A region-based hypergraph network for joint entity-relation extraction. Knowl-Based Syst 228(107):298. https:\/\/doi.org\/10.1016\/j.knosys.2021.107298","journal-title":"Knowl-Based Syst"},{"key":"5111_CR36","doi-asserted-by":"publisher","unstructured":"Xu B, Wang Q, Lyu Y, et\u00a0al (2022) EmRel: Joint representation of entities and embedded relations for multi-triple extraction. In: Proceedings of the 2022 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 659\u2013665. https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.48","DOI":"10.18653\/v1\/2022.naacl-main.48"},{"issue":"110","key":"5111_CR37","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.knosys.2023.110550","volume":"271","author":"C Gao","year":"2023","unstructured":"Gao C, Zhang X, Li L et al (2023) ERGM: A multi-stage joint entity and relation extraction with global entity match. Knowl-Based Syst 271(110):550. https:\/\/doi.org\/10.1016\/j.knosys.2023.110550","journal-title":"Knowl-Based Syst"},{"key":"5111_CR38","doi-asserted-by":"crossref","unstructured":"Sui D, Zeng X, Chen Y, et\u00a0al (2023) Joint Entity and Relation Extraction With Set Prediction Networks. IEEE Trans Neural Netw Learn Syst pp 1\u201312. 10.1109\/TNNLS.2023.3264735","DOI":"10.1109\/TNNLS.2023.3264735"},{"key":"5111_CR39","unstructured":"Vaswani A, Shazeer N, Parmar N, et\u00a0al (2017) Attention is all you need. In: Advances in neural information processing systems 30: annual conference on neural information processing systems, pp 5998\u20136008. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"5111_CR40","unstructured":"Liu Y, Ott M, Goyal N, et\u00a0al (2019) RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692"},{"key":"5111_CR41","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer Normalization. arXiv:1607.06450"},{"key":"5111_CR42","unstructured":"Su J (2019) Conditional text generation based on conditional layer normalization-Scientific Spaces. https:\/\/kexue.fm\/archives\/712 Accessed 07 Aug 2023"},{"key":"5111_CR43","doi-asserted-by":"publisher","unstructured":"Riedel S, Yao L, McCallum A (2010) Modeling Relations and Their Mentions without Labeled Text. In: Machine learning and knowledge discovery in databases, pp 148\u2013163. https:\/\/doi.org\/10.1007\/978-3-642-15939-8_10","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"5111_CR44","doi-asserted-by":"publisher","unstructured":"Gardent C, Shimorina A, Narayan S, et\u00a0al (2017) Creating training corpora for NLG micro-planners. In: Proceedings of the 55th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 179\u2013188. https:\/\/doi.org\/10.18653\/v1\/P17-1017","DOI":"10.18653\/v1\/P17-1017"},{"issue":"3","key":"5111_CR45","doi-asserted-by":"publisher","first-page":"3132","DOI":"10.1007\/s10489-021-02600-2","volume":"52","author":"T Lai","year":"2022","unstructured":"Lai T, Cheng L, Wang D et al (2022) RMAN: Relational multi-head attention neural network for joint extraction of entities and relations. Appl Intell 52(3):3132\u20133142. https:\/\/doi.org\/10.1007\/s10489-021-02600-2","journal-title":"Appl Intell"},{"key":"5111_CR46","doi-asserted-by":"crossref","unstructured":"Zeng X, He S, Zeng D, et\u00a0al (2019) Learning the extraction order of multiple relational facts in a sentence with reinforcement learning. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 367\u2013377. 10.18653\/v1\/D19-1035","DOI":"10.18653\/v1\/D19-1035"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05111-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05111-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05111-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T06:07:46Z","timestamp":1703743666000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05111-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,3]]},"references-count":46,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["5111"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05111-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,3]]},"assertion":[{"value":"12 October 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 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":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"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":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}