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

Natural Language Processing (490) Information Retrieval (214) Machine Learning (1004)

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 Kim 1 Author Orcid Image ;   Donghyeon Park 1 Author Orcid Image ;   Yonghwa Choi 1 Author Orcid Image ;   Kyubum Lee 1 Author Orcid Image ;   Byounggun Kim 2 Author Orcid Image ;   Minji Jeon 1 Author Orcid Image ;   Jihye Kim 3 Author Orcid Image ;   Aik Choon Tan 3 Author Orcid Image ;   Jaewoo Kang 1 Author Orcid Image
Article Authors Cited by (10) Tweetations (1) Metrics

Journals

  1. Jin Q, Yuan Z, Xiong G, Yu Q, Ying H, Tan C, Chen M, Huang S, Liu X, Yu S. Biomedical Question Answering: A Survey of Approaches and Challenges. ACM Computing Surveys 2023;55(2):1 View
  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