{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T16:25:44Z","timestamp":1785601544768,"version":"3.56.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T00:00:00Z","timestamp":1618012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T00:00:00Z","timestamp":1618012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976124"],"award-info":[{"award-number":["61976124"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s13042-021-01315-7","type":"journal-article","created":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T10:03:08Z","timestamp":1618048988000},"page":"721-733","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Biomedical event trigger extraction based on multi-layer residual BiLSTM and contextualized word representations"],"prefix":"10.1007","volume":"13","author":[{"given":"Hao","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8663-9870","authenticated-orcid":false,"given":"Mingyu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,10]]},"reference":[{"issue":"18","key":"1315_CR1","doi-asserted-by":"publisher","first-page":"i575","DOI":"10.1093\/bioinformatics\/bts407","volume":"28","author":"S Pyysalo","year":"2012","unstructured":"Pyysalo S, Ohta T, Miwa M, Cho H-C, Tsujii J, Ananiadou S (2012) Event extraction across multiple levels of biological organization. Bioinformatics 28(18):i575\u2013i581","journal-title":"Bioinformatics"},{"issue":"1","key":"1315_CR2","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.artmed.2015.03.004","volume":"64","author":"D Zhou","year":"2015","unstructured":"Zhou D, Zhong D (2015) A semi-supervised learning framework for biomedical event extraction based on hidden topics. Artif Intell Med 64(1):51\u201358","journal-title":"Artif Intell Med"},{"issue":"11","key":"1315_CR3","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1093\/bioinformatics\/btu061","volume":"30","author":"D Zhou","year":"2014","unstructured":"Zhou D, Zhong D, He Y (2014) Event trigger identification for biomedical events extraction using domain knowledge. Bioinformatics 30(11):1587\u20131594","journal-title":"Bioinformatics"},{"issue":"4","key":"1315_CR4","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TCBB.2017.2715016","volume":"15","author":"X He","year":"2017","unstructured":"He X, Li L, Liu Y, Yu X, Meng J (2017) A two-stage biomedical event trigger detection method integrating feature selection and word embeddings. IEEE\/ACM Trans Comput Biol Bioinf 15(4):1325\u20131332","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"11","key":"1315_CR5","first-page":"1","volume":"12","author":"W Zhou","year":"2020","unstructured":"Zhou W, Zhu Z (2020) A novel bnmf-dnn based speech reconstruction method for speech quality evaluation under complex environments. Int J Mach Learn Cybern 12(11):1\u201314","journal-title":"Int J Mach Learn Cybern"},{"key":"1315_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ins.2020.05.105","volume":"541","author":"T Zhang","year":"2020","unstructured":"Zhang T, Yang X, Wang X, Wang R (2020) Deep joint neural model for single image haze removal and color correction. Inf Sci 541:16\u201335","journal-title":"Inf Sci"},{"key":"1315_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01210-7","author":"Y Xiao","year":"2020","unstructured":"Xiao Y, Yin H, Duan T, Qi H, Zhang Y, Jolfaei A, Xia K (2020) An intelligent prediction model for ucg state based on dual-source LSTM. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-020-01210-7","journal-title":"Int J Mach Learn Cybern"},{"issue":"12","key":"1315_CR8","doi-asserted-by":"publisher","first-page":"2807","DOI":"10.1007\/s13042-020-01152-0","volume":"11","author":"Y Li","year":"2020","unstructured":"Li Y, Xu Z, Wang X, Wang X (2020) A bibliometric analysis on deep learning during 2007\u20132019. Int J Mach Learn Cybern 11(12):2807\u20132826","journal-title":"Int J Mach Learn Cybern"},{"key":"1315_CR9","doi-asserted-by":"publisher","first-page":"103294","DOI":"10.1016\/j.jbi.2019.103294","volume":"99","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Lin H, Yang Z, Wang J, Sun Y, Xu B, Zhao Z (2019) Neural network-based approaches for biomedical relation classification: a review. J Biomed Inform 99:103294","journal-title":"J Biomed Inform"},{"issue":"2","key":"1315_CR10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s12920-016-0203-8","volume":"9","author":"J Wang","year":"2016","unstructured":"Wang J, Zhang J, An Y, Lin H, Yang Z, Zhang Y, Sun Y (2016) Biomedical event trigger detection by dependency-based word embedding. BMC Med Genom 9(2):45","journal-title":"BMC Med Genom"},{"issue":"03","key":"1315_CR11","doi-asserted-by":"publisher","first-page":"1541001","DOI":"10.1142\/S0219720015410012","volume":"13","author":"Y Nie","year":"2015","unstructured":"Nie Y, Rong W, Zhang Y, Ouyang Y, Xiong Z (2015) Embedding assisted prediction architecture for event trigger identification. J Bioinform Comput Biol 13(03):1541001","journal-title":"J Bioinform Comput Biol"},{"key":"1315_CR12","first-page":"316","volume":"2017","author":"PV Rahul","year":"2017","unstructured":"Rahul PV, Sahu SK, Anand A (2017) Biomedical event trigger identification using bidirectional recurrent neural network based models. BioNLP 2017:316\u2013321","journal-title":"BioNLP"},{"key":"1315_CR13","doi-asserted-by":"crossref","unstructured":"He X, Li L, Wan J, Song D, Meng J, Wang Z (2018) Biomedical event trigger detection based on bilstm integrating attention mechanism and sentence vector. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp 651\u2013654","DOI":"10.1109\/BIBM.2018.8621217"},{"issue":"20","key":"1315_CR14","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1186\/s12859-018-2543-1","volume":"19","author":"Y Wang","year":"2018","unstructured":"Wang Y, Wang J, Lin H, Tang X, Zhang S, Li L (2018) Bidirectional long short-term memory with crf for detecting biomedical event trigger in fasttext semantic space. BMC Bioinform 19(20):507","journal-title":"BMC Bioinform"},{"key":"1315_CR15","doi-asserted-by":"crossref","unstructured":"Li L, Huang M, Liu Y, Qian S, He X (2019) Contextual label sensitive gated network for biomedical event trigger extraction. J Biomed Inform:103221","DOI":"10.1016\/j.jbi.2019.103221"},{"key":"1315_CR16","unstructured":"Wang C, Zhou SK, Cheng Z (2020) First image then video: a two-stage network for spatiotemporal video denoising. arXiv:2001.00346"},{"key":"1315_CR17","doi-asserted-by":"crossref","unstructured":"Claus M, van Gemert J (2019) Videnn: Deep blind video denoising. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) workshops, Long Beach, CA, pp 1843\u20131852","DOI":"10.1109\/CVPRW.2019.00235"},{"key":"1315_CR18","doi-asserted-by":"crossref","unstructured":"Huang YY, Wang WY (2017) Deep residual learning for weakly-supervised relation extraction. arXiv:1707.08866","DOI":"10.18653\/v1\/D17-1191"},{"key":"1315_CR19","first-page":"6481","volume":"33","author":"T Gui","year":"2019","unstructured":"Gui T, Zhang Q, Zhao L, Lin Y, Peng M, Gong J, Huang X (2019) Long short-term memory with dynamic skip connections. Proc AAAI Conf Artif Intell 33:6481\u20136488","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1315_CR20","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. arXiv:1802.05365","DOI":"10.18653\/v1\/N18-1202"},{"key":"1315_CR21","doi-asserted-by":"crossref","unstructured":"Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. arXiv:1603.01360","DOI":"10.18653\/v1\/N16-1030"},{"key":"1315_CR22","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv:1301.3781"},{"key":"1315_CR23","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha, Qatar, pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"1315_CR24","unstructured":"Moen S, Ananiadou TSS (2013) Distributional semantics resources for biomedical text processing. In: Proceedings of the 5th international symposium on languages in biology and medicine (LBM), Tokyo, Japan, pp 39\u201344"},{"key":"1315_CR25","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training. https:\/\/s3-us-west-2.amazonaws.com\/openai-assets\/research-covers\/language-unsupervised\/language_understanding_paper.pdf"},{"key":"1315_CR26","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"issue":"8","key":"1315_CR27","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"1315_CR28","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, Nevada, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1315_CR29","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv:1607.06450"},{"key":"1315_CR30","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555"},{"key":"1315_CR31","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"issue":"1","key":"1315_CR32","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":"1315_CR33","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"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01315-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01315-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01315-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T16:21:13Z","timestamp":1646065273000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01315-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,10]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["1315"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01315-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,10]]},"assertion":[{"value":"10 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}