Representation Learning for Natural Language Processing
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Abstract (arXiv)
This book aims to review and present the recent advances of distributed representation learning for NLP, including why representation learning can improve NLP, how representation learning takes part in various important topics of NLP, and what challenges are still not well addressed by distributed representation.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Feb 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1.
5 papers.
- 2021Machine learning and deep learningcited by 26
- 2023Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoningcited by 9
- 2022FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmcited by 8
- 2023Representation Transfer Learning via Multiple Pre-trained models for Linear Regressioncited by 2
- 2024Towards Robust Knowledge Representations in Multilingual LLMs for Equivalence and Inheritance based Consistent Reasoningcited by 2
- 2024Towards Robust Knowledge Representations in Multilingual LLMs for Equivalence and Inheritance based Consistent Reasoning18 Oct 2024
- 2023Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning6 Sep 2023
- 2023Representation Transfer Learning via Multiple Pre-trained models for Linear Regression25 May 2023
- 2022FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm31 May 2022
- 2021Machine learning and deep learning12 Apr 2021