The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Progress in Neural NLP: Modeling, Learning, and Reasoning
Open accessAbstract
Natural language processing (NLP) is a subfield of artificial intelligence that focuses on enabling computers to understand and process human languages. In the last five years, we have witnessed the rapid development of NLP in tasks such as machine translation, question-answering, and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data. In this paper, we will review the latest progress in the neural network-based NLP framework (neural NLP) from three perspectives: modeling, learning, and reasoning. In the modeling section, we will describe several fundamental neural network-based modeling paradigms, such as word embedding, sentence embedding, and sequence-to-sequence modeling, which are widely used in modern NLP engines. In the learning section, we will introduce widely used learning methods for NLP models, including supervised, semi-supervised, and unsupervised learning; multitask learning; transfer learning; and active learning. We view reasoning as a new and exciting direction for neural NLP, but it has yet to be well addressed. In the reasoning section, we will review reasoning mechanisms, including the knowledge, existing non-neural inference methods, and new neural inference methods. We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledge-driven neural NLP models to handle complex tasks. At the end of this paper, we will briefly outline our thoughts on the future directions of neural NLP.
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Cited by
Katz, D. M., Bommarito, M. J., Gao, S., & Arredondo, P. (2024) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2023.0254 · Cited by 420
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Zhu, T., Ye, D., Wang, W., Zhou, W., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3014246 · Cited by 219
Applications of natural language processing and large language models in materials discovery
Jiang, X., Wang, W., Tian, S., Wang, H., Lookman, T., & Su, Y. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01554-0 · Cited by 145
An analysis of protein language model embeddings for fold prediction
Villegas-Morcillo, A., Gomez, A. M., & Sanchez, V. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac142 · Cited by 46
RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
Wu, L., Gray, M., Dang, O., Xu, J., Fang, H., & Tong, W. (2023) · Experimental Biology and Medicine · doi:10.1177/15353702231220669 · Cited by 16
Metadata
- Authors
- Zhou, M., Duan, N., Liu, S., & Shum, H.-Y.
- Venue
- Engineering
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 6
- Issue
- 3
- Pages
- 275-290
- ISSN
- 2095-8099 (ISSN-L)
- OpenAlex
- W2999089077
- Cited by
- 281
- References
- 67
- FWCI
- 10.99
- Type
- journal article
- Language
- en
- Open access
- gold
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