Siamese BERT Architecture Model with attention mechanism for Textual Semantic Similarity-Reference-Cited by-同舟云学术

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Siamese BERT Architecture Model with attention mechanism for Textual Semantic Similarity

Published:2023-05-02 Issue:30 Volume:82 Page:46673-46694
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Li Ruihao,Cheng Lianglun,Wang Depei,Tan Junming

Funder

Jiangsu Provincial Key Research and Development Program

National Natural Science Foundation of China-Yunnan Joint Fund

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory of Prevention and Control for Severe Clinical Animal Diseases

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-15509-4.pdf

Reference47 articles.

1. Agarwal B, Ramampiaro H, Langseth H, Ruocco M (2018) A deep network model for paraphrase detection in short text messages. Inf Process Manag 54:922–937. https://doi.org/10.1016/j.ipm.2018.06.005

2. Ahmed U, Mukhiya SK, Srivastava G et al (2021) Attention-based deep entropy active learning using lexical algorithm for mental health treatment. Front Psychol 12:64–2347. https://doi.org/10.3389/fpsyg.2021.642347

3. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

4. Bowman SR, Gauthier J, Rastogi A et al (2016) A fast unified model for parsing and sentence understanding. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, pp 1466–1477

5. Bowman SR, Angeli G, Potts C, Manning CD (2015) A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 conference on empirical methods in natural language processing. Association for Computational Linguistics, Lisbon, Portugal, pp 632–642

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