This paper is in the following e-collection/theme issue:

Natural Language Processing (650) Information Retrieval (271) Machine Learning (1400) Linguistics, Comprehension, and Learning (52)

Published on in Vol 6, No 1 (2018): Jan-Mar

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

Authors of this article:

Seongsoon Kim1 Author Orcid Image ;   Donghyeon Park1 Author Orcid Image ;   Yonghwa Choi1 Author Orcid Image ;   Kyubum Lee1 Author Orcid Image ;   Byounggun Kim2 Author Orcid Image ;   Minji Jeon1 Author Orcid Image ;   Jihye Kim3 Author Orcid Image ;   Aik Choon Tan3 Author Orcid Image ;   Jaewoo Kang1 Author Orcid Image
Article Authors Cited by (11) Tweetations (1) Metrics

Journals

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  2. Hao T, Li X, He Y, Wang F, Qu Y. Recent progress in leveraging deep learning methods for question answering. Neural Computing and Applications 2022;34(4):2765 View
  3. Yoon W, Jackson R, Lagerberg A, Kang J, Wren J. Sequence tagging for biomedical extractive question answering. Bioinformatics 2022;38(15):3794 View
  4. Zhu X, Chen Y, Gu Y, Xiao Z. SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering. Frontiers in Neurorobotics 2022;16 View
  5. Saikh T, Ghosal T, Mittal A, Ekbal A, Bhattacharyya P. ScienceQA: a novel resource for question answering on scholarly articles. International Journal on Digital Libraries 2022;23(3):289 View
  6. Chen J, Wei Z, Wang J, Wang R, Gong C, Zhang H, Miao D. Supplementing domain knowledge to BERT with semi-structured information of documents. Expert Systems with Applications 2024;235:121054 View