{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T04:01:27Z","timestamp":1784260887448,"version":"3.55.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s13042-022-01655-y","type":"journal-article","created":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T07:11:37Z","timestamp":1664608297000},"page":"633-641","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Event detection based on the label attention mechanism"],"prefix":"10.1007","volume":"14","author":[{"given":"Qing","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7792-9983","authenticated-orcid":false,"given":"Yanghui","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jincai","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangquan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,1]]},"reference":[{"key":"1655_CR1","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"},{"key":"1655_CR2","unstructured":"Hogenboom FP, Frasincar F, Kaymak U et al (2011) An overview of event extraction from text"},{"key":"1655_CR3","doi-asserted-by":"crossref","unstructured":"D Ahn (2006) The stages of event extraction. In: Proc. workshop annotating reasoning about time events, pp\u00a01\u20138","DOI":"10.3115\/1629235.1629236"},{"key":"1655_CR4","unstructured":"HL Chieu, HT Ng (2002) A maximum entropy approach to information extraction from semi-structured and free text. In: Proc. 18th Nat. Conf. Artif. Intell., pp\u00a0786\u2013791"},{"issue":"1","key":"1655_CR5","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1142\/S0219720010004586","volume":"8","author":"M Miwa","year":"2010","unstructured":"Miwa M, S\u00e6tre R, Kim J-D, Tsujii J (2010) Event extraction with complex event classification using rich features. J Bioinf Comput Biol 8(1):131\u2013146","journal-title":"J Bioinf Comput Biol"},{"issue":"13","key":"1655_CR6","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1093\/bioinformatics\/bts237","volume":"28","author":"M Miwa","year":"2012","unstructured":"Miwa M, Thompson P, Ananiadou S (2012) Boosting automatic event extraction from the literature using domain adaptation and coreference resolution. Bioinformatics 28(13):1759\u20131765","journal-title":"Bioinformatics"},{"key":"1655_CR7","doi-asserted-by":"publisher","first-page":"205239","DOI":"10.1155\/2014\/205239","volume":"2014","author":"J Xia","year":"2014","unstructured":"Xia J, Fang AC, Zhang X (2014) A novel feature selection strategy for enhanced biomedical event extraction using the Turku system. BioMed Res Int 2014:205239","journal-title":"BioMed Res Int"},{"key":"1655_CR8","unstructured":"A Majumder, A Ekbal, SK Naskar (2016) Biomolecular event extraction using a stacked generalization based classifier. In: Proc. 13th Int. Conf. Natural Lang. Process., pp\u00a055\u201364"},{"key":"1655_CR9","doi-asserted-by":"crossref","unstructured":"Chen Y, Xu L, Liu K et al (2015) Event extraction via dynamic multi-pooling convolutional neural networks. In: The 53rd annual meeting of the association for computational linguistics (ACL2015)","DOI":"10.3115\/v1\/P15-1017"},{"key":"1655_CR10","doi-asserted-by":"crossref","unstructured":"Zhang Z, Xu W, Chen Q (2016) Joint event extraction based on skip-window convolutional neural networks","DOI":"10.1007\/978-3-319-50496-4_27"},{"key":"1655_CR11","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Grishman R (2016) Modeling skip-grams for event detection with convolutional neural networks. In: Conference on empirical methods in natural language processing","DOI":"10.18653\/v1\/D16-1085"},{"key":"1655_CR12","unstructured":"Sha L, Feng Q, Baobao C et al. Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction"},{"key":"1655_CR13","unstructured":"Xiao L, Luo Z, Huang H (2018) Jointly multiple events extraction via attention-based graph information aggregation. Emnlp"},{"key":"1655_CR14","doi-asserted-by":"crossref","unstructured":"K Huang, M Yang, N Peng (2020) Biomedical event extraction on graph edge-conditioned attention networks with hierarchical knowledge graphs. In: EMNLP","DOI":"10.18653\/v1\/2020.findings-emnlp.114"},{"key":"1655_CR15","doi-asserted-by":"crossref","unstructured":"Yang S, Feng D, Qiao L et al (2019) Exploring pre-trained language models for event extraction and generation. In: Proceedings of the 57th annual meeting of the association for computational linguistics","DOI":"10.18653\/v1\/P19-1522"},{"key":"1655_CR16","doi-asserted-by":"crossref","unstructured":"J Liu, Y Chen, K Liu, W Bi, X Liu (2020) Event extraction as machine reading comprehension. In: EMNLP","DOI":"10.18653\/v1\/2020.emnlp-main.128"},{"key":"1655_CR17","doi-asserted-by":"crossref","unstructured":"Huang P, Zhao X, Takanobu R et al (2020) Joint event extraction with hierarchical policy network. In: Proceedings of the 28th international conference on computational linguistics","DOI":"10.18653\/v1\/2020.coling-main.239"},{"issue":"20","key":"1655_CR18","first-page":"1","volume":"39","author":"JA Alzubi","year":"2020","unstructured":"Alzubi JA, Jain R, Kathuria A et al (2020) Paraphrase identification using collaborative adversarial networks. J Intell Fuzzy Syst 39(20):1\u201312","journal-title":"J Intell Fuzzy Syst"},{"key":"1655_CR19","doi-asserted-by":"crossref","unstructured":"Movassagh AA, Alzubi JA, Gheisari M et al (2021) Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Human Comput 1\u20139","DOI":"10.1007\/s12652-020-02623-6"},{"issue":"JUL.20","key":"1655_CR20","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2019.03.086","volume":"350","author":"PA Alaba","year":"2019","unstructured":"Alaba PA, Popoola SI, Olatomiwa L et al (2019) Towards a more efficient and cost-sensitive extreme learning machine: a state-of-the-art review of recent trend. Neurocomputing 350(JUL.20):70\u201390","journal-title":"Neurocomputing"},{"key":"1655_CR21","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Grishman R (2015) Event detection and domain adaptation with convolutional neural networks","DOI":"10.3115\/v1\/P15-2060"},{"key":"1655_CR22","first-page":"6851","volume":"33","author":"TM Nguyen","year":"2019","unstructured":"Nguyen TM, Nguyen TH (2019) One for all: neural joint modeling of entities and events. Proc AAAI Conf Artif Intell 33:6851\u20136858","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1655_CR23","doi-asserted-by":"crossref","unstructured":"Zhao Y, Jin X, Wang Y et al (2018) Document embedding enhanced event detection with hierarchical and supervised attention. In: Proceedings of the 56th annual meeting of the association for computational linguistics, vol\u00a02. Short Papers","DOI":"10.18653\/v1\/P18-2066"},{"key":"1655_CR24","doi-asserted-by":"crossref","unstructured":"Mehta S, Islam MR, Rangwala H et al (2019) Event detection using hierarchical multi-aspect attention. The World Wide Web Conference","DOI":"10.1145\/3308558.3313659"},{"key":"1655_CR25","unstructured":"Nsl DI (2019) Joint entity and event extraction with generative adversarial imitation learning"},{"key":"1655_CR26","unstructured":"Devlin J, Chang MW, Lee K et al (2018) BERT: pre-training of deep bidirectional transformers for language understanding"},{"key":"1655_CR27","first-page":"534","volume-title":"NLPCC 2020 LNCS (LNAI)","author":"X Li","year":"2020","unstructured":"Li X et al (2020) DuEE: a large-scale dataset for Chinese event extraction in real-world scenarios. In: Zhu X, Zhang M, Hong Yu, He R (eds) NLPCC 2020 LNCS (LNAI), vol 12431. Springer, Cham, pp 534\u2013545"},{"key":"1655_CR28","unstructured":"Xi X, Zhang T, Ye W, et al (2019) A hybrid character representation for chinese event detection. In: 2019 international joint conference on neural networks (IJCNN). IEEE"},{"key":"1655_CR29","doi-asserted-by":"crossref","unstructured":"Wadden D, Wennberg U, Luan Y et al (2019) Entity, relation, and event extraction with contextualized span representations","DOI":"10.18653\/v1\/D19-1585"},{"key":"1655_CR30","unstructured":"Loshchilov I, Hutter F (2017) Fixing weight decay regularization in Adam"},{"key":"1655_CR31","unstructured":"Technicolor T, Related S, Technicolor T et al. ImageNet classification with deep convolutional neural networks"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01655-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01655-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01655-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T09:03:46Z","timestamp":1674637426000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01655-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["1655"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01655-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,1]]},"assertion":[{"value":"30 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}