{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T18:08:49Z","timestamp":1760551729925,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172167"],"award-info":[{"award-number":["62172167"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s10489-022-03598-x","type":"journal-article","created":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T03:27:42Z","timestamp":1657337262000},"page":"6554-6568","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A syntactic distance sensitive neural network for event argument extraction"],"prefix":"10.1007","volume":"53","author":[{"given":"Lu","family":"Dai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0991-3597","authenticated-orcid":false,"given":"Yijun","family":"Mo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"key":"3598_CR1","doi-asserted-by":"crossref","unstructured":"Han J, Wang H (2022) A meta learning approach for open information extraction. Neural Computing and Applications","DOI":"10.1007\/s00521-022-07114-7"},{"key":"3598_CR2","doi-asserted-by":"crossref","unstructured":"Lin Y, Ji H, Huang F, Wu L (2020) A joint neural model for information extraction with global features. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 7999\u20138009","DOI":"10.18653\/v1\/2020.acl-main.713"},{"key":"3598_CR3","doi-asserted-by":"publisher","first-page":"173111","DOI":"10.1109\/ACCESS.2019.2956831","volume":"7","author":"W Xiang","year":"2019","unstructured":"Xiang W, Wang B (2019) A survey of event extraction from text. IEEE Access 7:173111\u2013173137","journal-title":"IEEE Access"},{"issue":"1","key":"3598_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-3376-2","volume":"21","author":"L Zhu","year":"2020","unstructured":"Zhu L, Zheng H (2020) Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks. BMC bioinformatics 21(1):1\u201312","journal-title":"BMC bioinformatics"},{"key":"3598_CR5","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.aiopen.2021.02.004","volume":"1","author":"K Liu","year":"2020","unstructured":"Liu K, Chen Y, Liu J, Zuo X (2020) Junzhao: Extracting event and their relations from texts: A survey on recent research progress and challenges. AI Open 1:22\u201339","journal-title":"AI Open"},{"key":"3598_CR6","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ins.2019.09.075","volume":"512","author":"H Fei","year":"2020","unstructured":"Fei H, Ren Y, Ji D (2020) A tree-based neural network model for biomedical event trigger detection. Inf Sci 512:175\u2013185","journal-title":"Inf Sci"},{"key":"3598_CR7","doi-asserted-by":"crossref","unstructured":"Lu S, Li S, Xu Y, Wang K, Lan H, Guo J (2021) Event detection from text using path-aware graph convolutional network. Appl Intell, pp 1\u201312","DOI":"10.1007\/s10489-021-02695-7"},{"issue":"11","key":"3598_CR8","doi-asserted-by":"publisher","first-page":"5805","DOI":"10.1007\/s00521-020-05360-1","volume":"33","author":"Z Wang","year":"2021","unstructured":"Wang Z, Guo Y, Wang J (2021) Empower chinese event detection with improved atrous convolution neural networks. Neural Comput & Applic 33(11):5805\u20135820","journal-title":"Neural Comput & Applic"},{"key":"3598_CR9","doi-asserted-by":"crossref","unstructured":"Vo T (2021) Synseq4ed: a novel event-aware text representation learning for event detection. Neural Process Lett, pp 1\u201323","DOI":"10.1007\/s11063-021-10627-2"},{"key":"3598_CR10","doi-asserted-by":"crossref","unstructured":"Wang X, Wang Z, Han X, Liu Z, Li J, Li P, Sun M, Zhou J, Ren X (2019) Hmeae: Hierarchical modular event argument extraction. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 5777\u20135783","DOI":"10.18653\/v1\/D19-1584"},{"key":"3598_CR11","unstructured":"Wang X, Jia S, Han X, Liu Z, Li J, Li P, Zhou J (2020) Neural gibbs sampling for joint event argument extraction. In: Proceedings of the 1st conference of the asia-pacific chapter of the association for computational linguistics and the 10th international joint conference on natural language processing, pp 169\u2013180"},{"key":"3598_CR12","unstructured":"Veyseh APB, Nguyen TN, Nguyen TH (2020) Graph transformer networks with syntactic and semantic structures for event argument extraction. In: Proceedings of the 2020 conference on empirical methods in natural language processing: findings, pp 3651\u20133661"},{"issue":"1","key":"3598_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-018-0723-6","volume":"19","author":"Z Li","year":"2019","unstructured":"Li Z, Yang Z, Shen C, Xu J, Zhang Y, Xu H (2019) Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text. BMC medical informatics and decision making 19(1):1\u20138","journal-title":"BMC medical informatics and decision making"},{"issue":"6","key":"3598_CR14","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1007\/s00521-020-05087-z","volume":"33","author":"Z Li","year":"2021","unstructured":"Li Z, Sun Y, Zhu J, Tang S, Zhang C, Ma H (2021) Improve relation extraction with dual attention-guided graph convolutional networks. Neural Comput & Applic 33(6):1773\u20131784","journal-title":"Neural Comput & Applic"},{"key":"3598_CR15","doi-asserted-by":"crossref","unstructured":"Sun Q, Zhang K, Lv L, Li X, Huang K, Zhang T (2021) Joint extraction of entities and overlapping relations by improved graph convolutional networks. Appl Intell, pp 1\u201313","DOI":"10.1007\/s10489-021-02667-x"},{"key":"3598_CR16","doi-asserted-by":"crossref","unstructured":"Sha L, Qian F, Chang B, Sui Z (2018) Jointly extracting event triggers and arguments by dependency-bridge rnn and tensor-based argument interaction. In: Proceedings of the 32rd AAAI Conference on Artificial Intelligence, pp 5916\u20135923","DOI":"10.1609\/aaai.v32i1.12034"},{"key":"3598_CR17","doi-asserted-by":"crossref","unstructured":"Liu X, Luo Z, Huang H (2018) Jointly multiple events extraction via attention-based graph information aggregation. In: Proceedings of the 2018 conference on empirical methods in Natural Language Processing, pp 1247\u20131256","DOI":"10.18653\/v1\/D18-1156"},{"key":"3598_CR18","unstructured":"Consortium LD (2005) Ace (automatic content extraction) english annotation guidelines for events"},{"key":"3598_CR19","unstructured":"Hong Y, Zhang J, Ma B, Yao J, Zhou G, Zhu Q (2011) Using cross-entity inference to improve event extraction. In: Proceedings of the 49th annual meeting of the association for computational linguistics, pp 1127\u20131136"},{"key":"3598_CR20","unstructured":"Chen C, Ng V (2012) Joint modeling for chinese event extraction with rich linguistic features. In: Proceedings of the 24th International Conference on Computational Linguistics, pp 529\u2013544"},{"key":"3598_CR21","unstructured":"Li Q, Ji H, Huang L (2013) Joint event extraction via structured prediction with global features. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp 73\u201382"},{"key":"3598_CR22","unstructured":"Li P, Zhu Q, Zhou G (2013) Argument inference from relevant event mentions in chinese argument extraction. In: Proceedings of the 51st annual meeting of the association for computational linguistics, pp 1477\u20131487"},{"key":"3598_CR23","doi-asserted-by":"crossref","unstructured":"Chen Y, Xu L, Liu K, Zeng D, Zhao J (2015) Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp 167\u2013176","DOI":"10.3115\/v1\/P15-1017"},{"key":"3598_CR24","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Cho K, Grishman R (2016) 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, pp 300\u2013309","DOI":"10.18653\/v1\/N16-1034"},{"key":"3598_CR25","doi-asserted-by":"crossref","unstructured":"Li D, Huang L, Ji H, Han J (2019) Biomedical event extraction based on knowledge-driven tree-lstm. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: Human Language Technologies, pp 1421\u20131430","DOI":"10.18653\/v1\/N19-1145"},{"key":"3598_CR26","doi-asserted-by":"crossref","unstructured":"Ma J, Wang S, Anubhai R, Ballesteros M, Al-Onaizan Y (2020) Resource-enhanced neural model for event argument extraction. In: Proceedings of the 2020 conference on empirical methods in natural language processing: findings, pp 3554\u20133559","DOI":"10.18653\/v1\/2020.findings-emnlp.318"},{"key":"3598_CR27","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st conference on neural information processing systems, pp 6000\u20136010"},{"key":"3598_CR28","doi-asserted-by":"crossref","unstructured":"Huang L, Ji H, Cho K, Dagan I, Riedel S, Voss C (2018) Zero-shot transfer learning for event extraction. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 2160\u20132170","DOI":"10.18653\/v1\/P18-1201"},{"key":"3598_CR29","doi-asserted-by":"crossref","unstructured":"Subburathinam A, Lu D, Ji H, May J, Chang S-F, Sil A, Voss C (2019) Cross-lingual structure transfer for relation and event extraction. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 313\u2013325","DOI":"10.18653\/v1\/D19-1030"},{"key":"3598_CR30","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang X, Han X, Lin Y, Hou L, Liu Z, Li P, Li J, Zhou J (2021) Cleve: Contrastive pre-training for event extraction. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, pp 6283\u20136297","DOI":"10.18653\/v1\/2021.acl-long.491"},{"key":"3598_CR31","doi-asserted-by":"crossref","unstructured":"Ferguson J, Lockard C, Weld D, Hajishirzi H (2018) Semi-supervised event extraction with paraphrase clusters. In: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 359\u2013364","DOI":"10.18653\/v1\/N18-2058"},{"key":"3598_CR32","doi-asserted-by":"crossref","unstructured":"Zhou Y, Chen Y, Zhao J, Wu Y, Xu J, Li J (2021) What the role is vs. what plays the role: Semi-supervised event argument extraction via dual question answering. In: Proceedings of the AAAI conference on artificial intelligence, pp 14638\u201314646","DOI":"10.1609\/aaai.v35i16.17720"},{"key":"3598_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Y, Qi P, Manning CD (2018) Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 2205\u20132215","DOI":"10.18653\/v1\/D18-1244"},{"issue":"1","key":"3598_CR34","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/s10115-018-1312-9","volume":"61","author":"S Zhang","year":"2019","unstructured":"Zhang S, Zhang W, Niu J (2019) Improving short-text representation in convolutional networks by dependency parsing. Knowl Inf Syst 61(1):463\u2013484","journal-title":"Knowl Inf Syst"},{"key":"3598_CR35","doi-asserted-by":"crossref","unstructured":"Wang C, Wang B, Xiang W, Xu M (2019) Encoding syntactic dependency and topical information for social emotion classification. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 881\u2013884","DOI":"10.1145\/3331184.3331287"},{"key":"3598_CR36","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2020.06.061","volume":"411","author":"Y Hong","year":"2020","unstructured":"Hong Y, Liu Y, Yang S, Zhang K, Hu J (2020) Joint extraction of entities and relations using graph convolution over pruned dependency trees. Neurocomputing 411:302\u2013312","journal-title":"Neurocomputing"},{"issue":"7","key":"3598_CR37","doi-asserted-by":"publisher","first-page":"4408","DOI":"10.1007\/s10489-020-02095-3","volume":"51","author":"Q Lu","year":"2021","unstructured":"Lu Q, Zhu Z, Zhang G, Kang S, Liu P (2021) Aspect-gated graph convolutional networks for aspect-based sentiment analysis. Appl Intell 51(7):4408\u20134419","journal-title":"Appl Intell"},{"key":"3598_CR38","unstructured":"Doddington G, Mitchell A, Przbocki M, Ramshaw L, Strassel S, Weischedel R (2004) The automatic content extraction (ace) program-tasks, data, and evaluation. In: Proceedings of the 4th international conference on language resources and evaluation, pp 837\u2013840"},{"key":"3598_CR39","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C. D (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"issue":"1","key":"3598_CR40","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"3598_CR41","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (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, pp 4171\u20134186"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03598-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03598-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03598-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T04:31:48Z","timestamp":1677472308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03598-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,9]]},"references-count":41,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["3598"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03598-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,7,9]]},"assertion":[{"value":"6 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have no financial or proprietary interests that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}