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A unified framework for attribute extraction in electronic medical records

Published: 09 March 2021 Publication History
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References

[1]
Ming Du, Minmin Pang, and Bo Xu: Multi-task Learning for Attribute Extraction from Unstructured Electronic Medical Records. In the 9th Joint International Semantic Technology Conference,2019
[2]
Hui Han, Eren Manavoglu, C. Lee Giles, and Hongyuan Zha. 2003. Rule-based word clustering for text classification. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval (SIGIR ’03). Association for Computing Machinery, New York, NY, USA, pp.445–446.
[3]
Tong S, Koller D . Support Vector Machine Active Learning with Applications to Text Classification[C]// JMLR.org, 2002, pp.999-1006.
[4]
Andrew McCallum, Kamal Nigam. A comparison of event models for Naive Bayes text classification[J]. Aaai Workshop on Learning for Text Categorization, 1998, pp.41–48.
[5]
Baoxun Xu, Xiufeng Guo, Yunming Ye. An improved random forest classifier for text categorization[J]. 2012.
[6]
Y. Kim, “Convolutional neural networks for sentence classification,” in EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2014.
[7]
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,”arXiv preprint arXiv:1810.04805, 2018.
[8]
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “Albert: A lite bert for self-supervised learning of language representations,” arXiv preprint arXiv:1909.11942, 2019.
[9]
Casey Whitelaw, Alex Kehlenbeck, Nemanja Petrovic, and Lyle Ungar. 2008. Web-scale named entity recognition. In Proceedings of the 17th ACM conference on Information and knowledge management (CIKM ’08). Association for Computing Machinery, New York, NY, USA, pp.123–132.
[10]
S. R. Eddy, “Hidden markov models,” Curr. Opin. Struct. Biol.,vol. 6, no. 3, pp. 361–365, 1996.
[11]
J. D. Lafferty, A. McCallum, and F. C. N. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” pp. 282–289, 2001.
[12]
Z. Huang, W. Xu, and K. Yu, “Bidirectional lstm-crf models for sequence tagging,” arXiv preprint arXiv:1508.01991, 2015.
[13]
Pan S J, Yang Q. A Survey on Transfer Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359.
[14]
Rich Caruana. 1998. Multitask learning. Learning to learn. Kluwer Academic Publishers, USA, pp.95–133.
[15]
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.
[16]
Mikolov T, Chen K, Corrado G, Efficient Estimation of Word Representations in Vector Space[J]. Computer ence, 2013.
[17]
Song, Y., Shi, S., Li, J., Zhang, H.: Directional skip-gram: Explicitly distinguishing left and right context for word embeddings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). pp. 175-180. Association for Computational Linguistics (2018).

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  • (2023)A semantic sequence similarity based approach for extracting medical entities from clinical conversationsInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10321360:2Online publication date: 1-Mar-2023

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cover image ACM Other conferences
ACAI '20: Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence
December 2020
576 pages
ISBN:9781450388115
DOI:10.1145/3446132
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Published: 09 March 2021

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Author Tags

  1. ALBERT
  2. Attribute extraction
  3. Multi-task Learning
  4. Unified framework

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  • Research-article
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  • Refereed limited

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  • Artificial intelligence innovation and development project of Shanghai Economic and Information Commission

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ACAI 2020

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Overall Acceptance Rate 173 of 395 submissions, 44%

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  • (2023)A semantic sequence similarity based approach for extracting medical entities from clinical conversationsInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10321360:2Online publication date: 1-Mar-2023

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