# Representation Learning for Natural Language Processing

arXiv: https://arxiv.org/abs/2102.03732 · HTML: https://mlsanity.com/paper/2102.03732/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2021.

## Built on by (most cited first, 5 of 5)

- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2309.03251/) (2023)
- [FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm](https://mlsanity.com/paper/2205.15485/) (2022)
- [Representation Transfer Learning via Multiple Pre-trained models for Linear Regression](https://mlsanity.com/paper/2305.16440/) (2023)
- [Towards Robust Knowledge Representations in Multilingual LLMs for Equivalence and Inheritance based Consistent Reasoning](https://mlsanity.com/paper/2410.14235/) (2024)

## 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.