Acceso abierto

Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts


Cite

Amini, I., Martinez, D., & Molla, D. (2012). Overview of the ALTA 2012 shared task. In Proceedings of the Australasian Language Technology Association Workshop 2012: ALTA 2012 (pp. 124–129). Dunedin, New Zealand.AminiI.MartinezD.&MollaD.2012Overview of the ALTA 2012 shared taskProceedings of the Australasian Language Technology Association Workshop 2012: ALTA 2012124129Dunedin, New ZealandSearch in Google Scholar

Badie, K., Asadi, N., & Tayefeh Mahmoudi, M. (2018). Zone identification based on features with high semantic richness and combining results of separate classifiers. Journal of Information and Telecommunication, 2(4), 411–427.BadieK.AsadiN.&Tayefeh MahmoudiM.2018Zone identification based on features with high semantic richness and combining results of separate classifiersJournal of Information and Telecommunication2441142710.1080/24751839.2018.1460083Search in Google Scholar

Basili, R. & Pennacchiotti, M. (2010). Distributional lexical semantics: Toward uniform representation paradigms for advanced acquisition and processing tasks. Natural Language Engineering, 1(1), 1–12.BasiliR.&PennacchiottiM.2010Distributional lexical semantics: Toward uniform representation paradigms for advanced acquisition and processing tasksNatural Language Engineering1111210.1017/S1351324910000112Search in Google Scholar

Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: Pretrained contextualized embeddings for scientific text. arXiv:1903.10676v3.BeltagyI.LoK.&CohanA.2019SciBERT: Pretrained contextualized embeddings for scientific textarXiv:1903.10676v3Search in Google Scholar

Dasigi, P., Burns, G.A.P.C., Hovy, E., & Waard, A. (2017). Experiment segmentation in scientific discourse as clause-level structured prediction using recurrent neural networks. arXiv:1702.05398.DasigiP.BurnsG.A.P.C.HovyE.&WaardA.2017Experiment segmentation in scientific discourse as clause-level structured prediction using recurrent neural networksarXiv:1702.05398Search in Google Scholar

Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805.DevlinJ.ChangM.W.LeeK.&ToutanovaK.2018Bert: Pre-training of deep bidirectional transformers for language understandingarXiv:1810.04805Search in Google Scholar

Ding, L.P., Zhang, Z.X., & Liu, H. (2019). Research on factors affecting the SVM model performance on move recognition. Data Analysis and Konwledge Discovery, http://kns.cnki.net/kcms/detail/10.1478.G2.20191012.0931.002.html.DingL.P.ZhangZ.X.&LiuH.2019Research on factors affecting the SVM model performance on move recognition. Data Analysis and Konwledge Discoveryhttp://kns.cnki.net/kcms/detail/10.1478.G2.20191012.0931.002.htmlSearch in Google Scholar

Firth, J.R. (1930). A synopsis of linguistic theory, 1930–1955. In: Firth, J.R., Ed., Studies in Linguistic Analysis, Longmans, London, 168–205.FirthJ.R.1930A synopsis of linguistic theory, 1930–1955FirthJ.R.Studies in Linguistic AnalysisLongmansLondon168205Search in Google Scholar

Fisas, B., Ronzano, F., & Saggion, H. (2016). A multi-layered annotated corpus of scientific papers. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016).FisasB.RonzanoF.&SaggionH.2016A multi-layered annotated corpus of scientific papersProceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)Search in Google Scholar

Franck Dernoncourt & Ji Young Lee. (2017). Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts. In Proceedings of the 8th International Joint Conference on Natural Language Processing.FranckDernoncourt&JiYoung Lee.2017Pubmed 200k rct: a dataset for sequential sentence classification in medical abstractsProceedings of the 8th International Joint Conference on Natural Language ProcessingSearch in Google Scholar

Gerlach, M., Peixoto, T.P., Altmann, E.G., & Altmann, E.G. (2018). A network approach to topic models. Science advances, 4(7), eaaq1360.GerlachM.PeixotoT.P.AltmannE.G.&AltmannE.G.2018A network approach to topic modelsScience advances4710.1126/sciadv.aaq1360605174230035215Search in Google Scholar

Hirohata, K., Okazaki, N., Ananiadou, S., & Mitsuru. (2018). Identifying sections in scientific abstracts using conditional random fields. In Proceedings of the Third International Joint Conference on Natural Language Processing.HirohataK.OkazakiN.AnaniadouS.& Mitsuru2018Identifying sections in scientific abstracts using conditional random fieldsProceedings of the Third International Joint Conference on Natural Language ProcessingSearch in Google Scholar

Ma, M.B., Huang, L., Xiang, B., & Zhou, B.W. (2015). Dependency-based convolutional neural networks for sentence embedding. arXiv:1507.01839.MaM.B.HuangL.XiangB.&ZhouB.W.2015Dependency-based convolutional neural networks for sentence embeddingarXiv:1507.0183910.3115/v1/P15-2029Search in Google Scholar

Peters, M.E., Neumann, M., Iyyer, M., et al. (2018). Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. doi: 10.18653/v1/N18-1202 arXiv:1802.05365.PetersM.E.NeumannM.IyyerM.et al2018Deep contextualized word representationsProceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies10.18653/v1/N18-1202arXiv:1802.05365Open DOISearch in Google Scholar

Radford, A., Narasimhan, K., Salimans, T., & Sutskever Ilya (2018). Improving language understanding by generative pre-training. https://s3-us-west-2.amazonaws.com/openai-assets/researchcovers/languageunsupervised/languageunderstandingpaper.pdfRadfordA.NarasimhanK.SalimansT.&SutskeverIlya2018Improving language understanding by generative pre-traininghttps://s3-us-west-2.amazonaws.com/openai-assets/researchcovers/languageunsupervised/languageunderstandingpaper.pdfSearch in Google Scholar

Lai, S.W., Xu, L., Liu, K., & Zhao, J. (2015). Recurrent convolutional neural networks for text classification. In AAAI’15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pages 2267–2273.LaiS.W.XuL.LiuK.&ZhaoJ.2015Recurrent convolutional neural networks for text classificationAAAI’15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence2267227310.1609/aaai.v29i1.9513Search in Google Scholar

Swales, J.M. (2004). Research genres: Explorations and applications. Cambridge: Cambridge University Press.SwalesJ.M.2004Research genres: Explorations and applicationsCambridgeCambridge University Press10.1017/CBO9781139524827Search in Google Scholar

Taylor, W.L. (1953). “Cloze procedure”: A new tool for measuring readability. Journalism & Mass Communication Quarterly, 30(4), 415–433. doi: https://doi.org/10.1177/107769905303000401TaylorW.L.1953“Cloze procedure”: A new tool for measuring readabilityJournalism & Mass Communication Quarterly304415433https://doi.org/10.1177/10776990530300040110.1177/107769905303000401Search in Google Scholar

Teufel, S. (1999). Argumentative zoning: Information extraction from scientific text. Edinburgh: University of Edinburgh.TeufelS.1999Argumentative zoning: Information extraction from scientific textEdinburghUniversity of EdinburghSearch in Google Scholar

Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. arXiv:1706.03762v5.VaswaniA.ShazeerN.ParmarN.et al2017Attention is all you needarXiv:1706.03762v5Search in Google Scholar

Yamamoto, Y. & Takagi, T. (2005). A sentence classification system for multi-document summarization in the biomedical domain. In Proceedings of International Workshop on Biomedical Data Engineering, pages 90–95.YamamotoY.&TakagiT.2005A sentence classification system for multi-document summarization in the biomedical domainProceedings of International Workshop on Biomedical Data Engineering909510.1109/ICDE.2005.170Search in Google Scholar

Yoon Kim. (2014). Convolutional neural networks for sentence classification. arXiv:1408.5882.YoonKim.2014Convolutional neural networks for sentence classificationarXiv:1408.588210.3115/v1/D14-1181Search in Google Scholar

Zhang, Z., Liu, H., Ding, L., et al. (2019). Moves recognition in abstract of research paper based on deep learning. In Proceedings of 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL). IEEE, pages 390–391.ZhangZ.LiuH.DingL.et al2019Moves recognition in abstract of research paper based on deep learningProceedings of 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)IEEE39039110.1109/JCDL.2019.00085Search in Google Scholar

eISSN:
2543-683X
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Computer Sciences, Information Technology, Project Management, Databases and Data Mining