From text to graph: a general transition-based AMR parsing using neural network-Reference-Cited by-同舟云学术

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From text to graph: a general transition-based AMR parsing using neural network

Published:2020-10-15 Issue:11 Volume:33 Page:6009-6025
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Gu Min,Gu Yanhui,Luo Weilan,Xu Guandong,Yang Zhenglu,Zhou Junsheng,Qu Weiguang

Funder

the Ministry of education of Humanities and Social Science project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-020-05378-5.pdf

Reference60 articles.

1. Zong Y, Xu G, Dolog P, Zhang Y, Liu R (2010) Co-clustering for Weblogs in Semantic Space. In: the 11th international conference of web information systems engineering, WISE 2010, Hong Kong, China, December 12–14. pp 120–127

2. Xu G, Yu JX, Lee W (2013) Social networks and social web mining. World Wide Web Internet Web Inf Syst 16(5–6):541–544

3. Li C, Goldwasser D (2019) Encoding social information with graph convolutional networks for political perspective detection in news media. In: the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, July 28–August 2, pp 2594–2604

4. Islam J, Mercer RE, Xiao L (2019) Multi-Channel convolutional neural network for twitter emotion and sentiment recognition. In: the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2–7, pp 1355–1365

5. Gupta I, Joshi N (2020) Enhanced twitter sentiment analysis using hybrid approach and by accounting local contextual semantic. J Intell Syst 29(1):1611–1625

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