Multi-granularity Hierarchical Feature Extraction for Question-Answering Understanding-Reference-Cited by-同舟云学术

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Multi-granularity Hierarchical Feature Extraction for Question-Answering Understanding

Published:2022-12-26 Issue:1 Volume:15 Page:121-131
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Qin Xingguo,Zhou Ya,Huang Guimin,Li Maolin,Li Jun^ORCID

Funder

Natural Science Foundation of Guangxi Province

Guangxi Key Laboratory of Image and Graphic Intelligent Processing

National Natural Science Foundation of China

Middle-aged and Young Teachers' Basic Ability Promotion Project of Guangxi

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-022-10102-7.pdf

Reference46 articles.

1. Cambazoglu BB, Sanderson M, Scholer F, Croft B. A review of public datasets in question answering research. In: ACM SIGIR Forum, vol. 54. ACM New York, NY, USA; 2021. p. 1–23.

2. Acheampong KN, Tian W. Advancement of textual answer triggering: cognitive boosting. IEEE Trans Emerg Top Comput. 2022;10(1):361–72.

3. Etezadi R, Shamsfard M. The state of the art in open domain complex question answering: a survey. Appl Intell. 2022;1–21.

4. Hao T, Li X, He Y, Wang FL, Qu Y. Recent progress in leveraging deep learning methods for question answering. Neural Comput Applic. 2022;1–19.

5. Luo Y, Yang B, Xu D, Tian L. A survey: complex knowledge base question answering. In: 2022 IEEE 2nd International Conference on Information Communication and Software Engineering (ICICSE). IEEE; 2022. p. 46–52.

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