{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T17:58:24Z","timestamp":1712771904646},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s12559-023-10208-6","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T16:02:05Z","timestamp":1698681725000},"page":"507-516","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph-Based Interactive Matching for Pairs of News Articles"],"prefix":"10.1007","volume":"16","author":[{"given":"Kunhao","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guowei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"10208_CR1","doi-asserted-by":"crossref","unstructured":"Yang Y, Carbonell J, Brown R, Lafferty J, Pierce T, Ault T. Multi-strategy learning for topic detection and tracking. In: Topic Detection and Tracking. Springer; 2002. p. 85\u2013114.","DOI":"10.1007\/978-1-4615-0933-2_5"},{"key":"10208_CR2","doi-asserted-by":"crossref","unstructured":"Zhou D, Xu H, He Y. An unsupervised Bayesian modelling approach for storyline detection on news articles. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015. p. 1943\u20138.","DOI":"10.18653\/v1\/D15-1225"},{"key":"10208_CR3","doi-asserted-by":"crossref","unstructured":"Br\u00fcggermann D, Hermey Y, Orth C, Schneider D, Selzer S, Spanakis G. Storyline detection and tracking using dynamic latent Dirichlet allocation. In: Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016). 2016. p. 9\u201319.","DOI":"10.18653\/v1\/W16-5702"},{"key":"10208_CR4","doi-asserted-by":"crossref","unstructured":"Robertson S, Zaragoza H, et\u00a0al. The probabilistic relevance framework: BM25 and beyond. Found Trends\u00ae Inf Ret. 2009;3(4):333\u2013389.","DOI":"10.1561\/1500000019"},{"key":"10208_CR5","unstructured":"Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3(Jan):993\u20131022."},{"key":"10208_CR6","doi-asserted-by":"crossref","unstructured":"Huang P-S, He X, Gao J, Deng L, Acero A, Heck L. Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. ACM; 2013. p. 2333\u20138.","DOI":"10.1145\/2505515.2505665"},{"key":"10208_CR7","doi-asserted-by":"crossref","unstructured":"Shen Y, He X, Gao J, Deng L, Mesnil G. Learning semantic representations using convolutional neural networks for web search. In: Proceedings of the 23rd International Conference on World Wide Web. ACM; 2014. p. 373\u20134.","DOI":"10.1145\/2567948.2577348"},{"key":"10208_CR8","doi-asserted-by":"crossref","unstructured":"Mitra B, Diaz F, Craswell N. Learning to match using local and distributed representations of text for web search. In: Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee; 2017. p. 1291\u20139.","DOI":"10.1145\/3038912.3052579"},{"key":"10208_CR9","unstructured":"Qiu X, Huang X. Convolutional neural tensor network architecture for community-based question answering. In: Twenty-Fourth International Joint Conference on Artificial Intelligence. 2015."},{"key":"10208_CR10","doi-asserted-by":"crossref","unstructured":"Wan S, Lan Y, Guo J, Xu J, Pang L, Cheng X. A deep architecture for semantic matching with multiple positional sentence representations. In: Thirtieth AAAI Conference on Artificial Intelligence. 2016.","DOI":"10.1609\/aaai.v30i1.10342"},{"key":"10208_CR11","doi-asserted-by":"crossref","unstructured":"Mueller J, Thyagarajan A. Siamese recurrent architectures for learning sentence similarity. In Thirtieth AAAI Conference on Artificial Intelligence. 2016.","DOI":"10.1609\/aaai.v30i1.10350"},{"key":"10208_CR12","unstructured":"Hu B, Lu Z, Li H, Chen Q. Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems. 2014. p. 2042\u201350."},{"key":"10208_CR13","doi-asserted-by":"crossref","unstructured":"Pang L, Lan Y, Guo J, Xu J, Wan S, Cheng X. Text matching as image recognition. In: Thirtieth AAAI Conference on Artificial Intelligence. 2016.","DOI":"10.1609\/aaai.v30i1.10341"},{"key":"10208_CR14","doi-asserted-by":"crossref","unstructured":"Wang Z, Hamza W, Florian R. Bilateral multi-perspective matching for natural language sentences. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17. 2017. p. 4144\u201350.","DOI":"10.24963\/ijcai.2017\/579"},{"key":"10208_CR15","doi-asserted-by":"crossref","unstructured":"Chen H, Han FX, Niu D, Liu D, Lai K, Wu C, Xu Y. Mix: Multi-channel information crossing for text matching. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM; 2018. p. 110\u20139.","DOI":"10.1145\/3219819.3219928"},{"key":"10208_CR16","unstructured":"Gong Y, Luo H, Zhang J. Natural language inference over interaction space. In: International Conference on Learning Representations. 2018."},{"key":"10208_CR17","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR). 2017."},{"issue":"1","key":"10208_CR18","first-page":"3","volume":"22","author":"Z Yanyan","year":"2008","unstructured":"Yanyan Z, Bing Q, Wan-Xiang C, Ting L. Research on Chinese event extraction. Journal of Chinese Information Processing. 2008;22(1):3\u20138.","journal-title":"Journal of Chinese Information Processing."},{"key":"10208_CR19","unstructured":"Walker C, Strassel S, Medero J, Maeda K. ACE 2005 Multilingual Training Corpus LDC2006T06. In: Web Download. Philadelphia: Linguistic Data Consortium; 2006."},{"key":"10208_CR20","unstructured":"Getman J, Ellis J, Song Z, Tracey J, Strassel SM. Overview of linguistic resources for the TAC KBP 2017 evaluations: methodologies and results. In: TAC. 2017."},{"issue":"01","key":"10208_CR21","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1142\/S0219720010004586","volume":"8","author":"M Makoto","year":"2010","unstructured":"Makoto M, Rune S, Jin-Dong K, Jun\u2019ichi T. Event extraction with complex event classification using rich features. J Bioinform Comput Biol. 2010;8(01):131\u201346.","journal-title":"J. Bioinform. Comput. Biol."},{"issue":"3","key":"10208_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2967502","volume":"8","author":"G Yue","year":"2017","unstructured":"Yue G, Hanwang Z, Xibin Z, Shuicheng Y. Event classification in microblogs via social tracking. ACM Trans Intell Syst Technol (TIST). 2017;8(3):1\u201314.","journal-title":"ACM Trans Intell Syst Technol (TIST)."},{"key":"10208_CR23","unstructured":"Yubo C, Liheng X, Kang L, Daojian Z, Jun Z, et\u00a0al. Event extraction via dynamic multi-pooling convolutional neural networks. 2015."},{"key":"10208_CR24","doi-asserted-by":"crossref","unstructured":"Yang S, Feng D, Qiao L, Kan Z, Li D. Exploring pre-trained language models for event extraction and generation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. p. 5284\u201394.","DOI":"10.18653\/v1\/P19-1522"},{"key":"10208_CR25","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Cho K, Grishman R. Joint event extraction via recurrent neural networks. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016. p. 300\u20139.","DOI":"10.18653\/v1\/N16-1034"},{"key":"10208_CR26","doi-asserted-by":"crossref","unstructured":"Wang Y, Ni X, Sun J-T, Tong Y, Chen Z. Representing document as dependency graph for document clustering. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. 2011. p. 2177\u201380.","DOI":"10.1145\/2063576.2063920"},{"key":"10208_CR27","unstructured":"Leskovec J, Grobelnik M, Milic-Frayling N. Learning sub-structures of document semantic graphs for document summarization. In: LinkKDD Workshop. 2004. p. 133\u20138."},{"key":"10208_CR28","doi-asserted-by":"crossref","unstructured":"Zhang T, Liu B, Niu D, Lai K, Xu Y. Multiresolution graph attention networks for relevance matching. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM; 2018. p. 933\u201342.","DOI":"10.1145\/3269206.3271806"},{"key":"10208_CR29","doi-asserted-by":"crossref","unstructured":"Nikolentzos G, Meladianos P, Rousseau F, Stavrakas Y, Vazirgiannis M. Shortest-path graph kernels for document similarity. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017. p. 1890\u2013900.","DOI":"10.18653\/v1\/D17-1202"},{"issue":"13","key":"10208_CR30","doi-asserted-by":"publisher","first-page":"6462","DOI":"10.1016\/j.jfranklin.2021.06.009","volume":"358","author":"X Yang","year":"2021","unstructured":"Yang X, Liao L, Yang Q, Sun B, Xi J. Limited-energy output formation for multiagent systems with intermittent interactions. J Franklin Inst. 2021;358(13):6462\u201389. Elsevier.","journal-title":"J Franklin Inst"},{"key":"10208_CR31","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1007\/s10115-003-0118-5","volume":"6","author":"KM Hammouda","year":"2003","unstructured":"Hammouda KM, Kamel MS. Document similarity using a phrase indexing graph model. Knowl Inf Syst. 2003;6:710\u201327.","journal-title":"Knowl. Inf. Syst."},{"key":"10208_CR32","doi-asserted-by":"crossref","unstructured":"Schenker A, Last M, Bunke H, Kandel A. Clustering of web documents using a graph model. In: Web Document Analysis. 2003.","DOI":"10.1142\/9789812775375_0001"},{"key":"10208_CR33","doi-asserted-by":"publisher","first-page":"118810","DOI":"10.1016\/j.ins.2023.03.035","volume":"644","author":"X Yang","year":"2023","unstructured":"Yang X, Zhu M, Cai Y, Wang Z, Nie F. Fast spectral clustering with self-adapted bipartite graph learning. Inf Sci. 2023;644:118810. Elsevier.","journal-title":"Inf Sci"},{"key":"10208_CR34","doi-asserted-by":"crossref","unstructured":"Putra JWG, Tokunaga T. Evaluating text coherence based on semantic similarity graph. In: TextGraphs@ACL. 2017.","DOI":"10.18653\/v1\/W17-2410"},{"key":"10208_CR35","doi-asserted-by":"crossref","unstructured":"Liu B, Niu D, Wei H, Lin J, He Y, Lai K, Xu Y. Matching article pairs with graphical decomposition and convolutions. In: Proceedings of the 57th Conference of the Association for Computational Linguistics. 2019. p. 6284\u201394.","DOI":"10.18653\/v1\/P19-1632"},{"key":"10208_CR36","doi-asserted-by":"crossref","unstructured":"G\u00f3mez MM, L\u00f3pez-L\u00f3pez A, Gelbukh A. Information retrieval with conceptual graph matching. In: International Conference on Database and Expert Systems Applications. Springer; 2000. p. 312\u201321.","DOI":"10.1007\/3-540-44469-6_29"},{"key":"10208_CR37","doi-asserted-by":"crossref","unstructured":"Haghighi AD, Ng AY, Manning CD. Robust textual inference via graph matching. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics; 2005. p. 387\u201394.","DOI":"10.3115\/1220575.1220624"},{"issue":"1","key":"10208_CR38","first-page":"1","volume":"1","author":"K Sandra","year":"2009","unstructured":"Sandra K, Ryan M, Joakim N. Dependency parsing. Synth Lect Hum Lang Technol. 2009;1(1):1\u2013127.","journal-title":"Synth Lect Hum Lang Technol"},{"key":"10208_CR39","doi-asserted-by":"crossref","unstructured":"Wities R, Shwartz V, Stanovsky G, Adler M, Shapira O, Upadhyay S, Roth D, Mart\u00ednez-C\u00e1mara E, Gurevych I, Dagan I. A consolidated open knowledge representation for multiple texts. In: Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-Level Semantics. 2017. p. 12\u201324.","DOI":"10.18653\/v1\/W17-0902"},{"key":"10208_CR40","unstructured":"Jacob D, Ming-Wei C, Kenton L, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT. 2019."},{"key":"10208_CR41","doi-asserted-by":"crossref","unstructured":"Manning C, Surdeanu M, Bauer J, Finkel J, Bethard S, McClosky D. The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2014. p. 55\u201360.","DOI":"10.3115\/v1\/P14-5010"},{"key":"10208_CR42","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C. GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014. p. 1532\u201343.","DOI":"10.3115\/v1\/D14-1162"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-023-10208-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-023-10208-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-023-10208-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T08:13:54Z","timestamp":1706256834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-023-10208-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10208"],"URL":"https:\/\/doi.org\/10.1007\/s12559-023-10208-6","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]},"assertion":[{"value":"3 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2023","order":4,"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 no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}