An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

University of Michigan - Ann Arbor

Book:
- Speech and Language Processing (3rd ed. draft) Written by Dan Jurafsky and James H. Martin
- Speech and Language Processing (2nd edition) Written by Dan Jurafsky and James H. Martin
Journal:
- A Survey on Dynamic Neural Networks for Natural Language Processing
- A Survey of Race, Racism, and Anti-Racism in NLP

NLP references

Speech and Language Processing (3rd ed. draft) 

An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
https://www.cs.colorado.edu/~martin/slp2.html

Speech and Language Processing (2nd edition)

Wang, R., Tan, X., Luo, R., Qin, T., & Liu, T.-Y. (8 2021). A Survey on Low-Resource Neural Machine Translation. Στο Z.-H. Zhou (Επιμ.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 (σσ. 4636–4643). doi:10.24963/ijcai.2021/629


A Survey on Low-Resource Neural Machine Translation

Anjalie Field, Su Lin Blodgett, Zeerak Waseem, and Yulia Tsvetkov. 2021. A Survey of Race, Racism, and Anti-Racism in NLP. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1905–1925, Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.149

A Survey of Race, Racism, and Anti-Racism in NLP

Khalilia, H., Freihat, A. A., & Giunchiglia, F. (2021). The Quality of Lexical Semantic Resources: A Survey. In Proceedings of The Fourth International Conference on Natural Language and Speech Processing (ICNLSP 2021) (pp. 117-129).

The Quality of Lexical Semantic Resources: A Survey

Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

Wu et al. (2021). Natural Language Processing Meets Quantum Physics: A Survey and Categorization. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.254

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