# Joint Embedding of Words and Labels for Text Classification

**Authors:** Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin  
**arXiv:** [1805.04174](https://arxiv.org/abs/1805.04174)  
**Date:** 2018-05-10  
**Summary page:** https://www.summarizepaper.com/en/paper/1805.04174v1/

## Key points

- Authors: Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
- Introduction of a novel approach to text classification using word embeddings
- Utilization of word embeddings as intermediate representations to capture semantic regularities between words in text sequences
- Proposal of a label-word joint embedding framework embedding each label in the same space as word vectors
- Introduction of an attention mechanism to measure compatibility of embeddings between text sequences and labels
- Training the attention mechanism on labeled samples to prioritize relevant words over irrelevant ones within a given text sequence
- Demonstrated superior performance compared to state-of-the-art methods in terms of accuracy and speed
- Extensive results on various large text datasets showcasing the effectiveness of the framework
- Availability of code for their approach on GitHub for further exploration and implementation

## Summary

In their paper "Joint Embedding of Words and Labels for Text Classification," authors Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, and Lawrence Carin introduce a novel approach to text classification using word embeddings. This method utilizes word embeddings as intermediate representations to capture semantic regularities between words in text sequences. The authors propose a label-word joint embedding framework where each label is embedded in the same space as word vectors. To enhance the classification process, an attention mechanism is introduced to measure the compatibility of embeddings between text sequences and labels. This mechanism is trained on labeled samples to prioritize relevant words over irrelevant ones within a given text sequence. By maintaining the interpretability of word embeddings and incorporating alternative sources of information alongside input text sequences, this approach demonstrates superior performance compared to state-of-the-art methods in terms of both accuracy and speed. The research conducted by Wang et al., published in ACL 2018, showcases extensive results on various large text datasets that highlight the effectiveness of their framework. The code for their approach is publicly available on GitHub for further exploration and implementation. Overall,this innovative approach offers a promising avenue for leveraging word embeddings and attention mechanisms to improve classification accuracy and efficiency in natural language processing tasks.