xwjzds/ag_news_lemma_train

# Dataset Card for Dataset Name

### Dataset Summary

This is lemmatized version of Ag News Data.

### Languages

English

### Citation Information

```bibtex
@inproceedings{xu-etal-2023-vontss,
title = "v{ONTSS}: v{MF} based semi-supervised neural topic modeling with optimal transport",
author = "Xu, Weijie and
Jiang, Xiaoyu and
Sengamedu Hanumantha Rao, Srinivasan and
Iannacci, Francis and
Zhao, Jinjin",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.271",
doi = "10.18653/v1/2023.findings-acl.271",
pages = "4433--4457",
abstract = "Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge. This work presents a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and optimal transport. When a few keywords per topic are provided, vONTSS in the semi-supervised setting generates potential topics and optimizes topic-keyword quality and topic classification. Experiments show that vONTSS outperforms existing semi-supervised topic modeling methods in classification accuracy and diversity. vONTSS also supports unsupervised topic modeling. Quantitative and qualitative experiments show that vONTSS in the unsupervised setting outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered and coherent topics on benchmark datasets. It is also much faster than the state-of-the-art weakly supervised text classification method while achieving similar classification performance. We further prove the equivalence of optimal transport loss and cross-entropy loss at the global minimum.",
}
```

原始信息汇总

数据集概述

数据集名称

Dataset Name

数据集摘要

这是一个经过词形还原处理的Ag News数据集版本。

语言

英语

引用信息

bibtex @inproceedings{xu-etal-2023-vontss, title = "v{ONTSS}: v{MF} based semi-supervised neural topic modeling with optimal transport", author = "Xu, Weijie and Jiang, Xiaoyu and Sengamedu Hanumantha Rao, Srinivasan and Iannacci, Francis and Zhao, Jinjin", booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.findings-acl.271", doi = "10.18653/v1/2023.findings-acl.271", pages = "4433--4457", abstract = "Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge. This work presents a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and optimal transport. When a few keywords per topic are provided, vONTSS in the semi-supervised setting generates potential topics and optimizes topic-keyword quality and topic classification. Experiments show that vONTSS outperforms existing semi-supervised topic modeling methods in classification accuracy and diversity. vONTSS also supports unsupervised topic modeling. Quantitative and qualitative experiments show that vONTSS in the unsupervised setting outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered and coherent topics on benchmark datasets. It is also much faster than the state-of-the-art weakly supervised text classification method while achieving similar classification performance. We further prove the equivalence of optimal transport loss and cross-entropy loss at the global minimum.", }