为支持中文短文本分类（STC）研究，我们发布了两个大规模的中文短文本语料库作为基准测试。详细信息如下。如果您认为此项目有价值，请考虑给我们点个星⭐。
To support research in Chinese short text classification (STC), we have released two large-scale Chinese short text corpora as benchmarks. Details are provided below. If you find this project valuable, please consider leaving a star⭐.
二分类情感识别，10万条中文弹幕消息，有两个情感类别：积极的和消极的，分成训练集（70%），验证集（15%）和测试集（15%）。
A large-scale dataset of 100K bullet screen messages was collected from short-video platforms for binary sentiment classification (Positive/Negative). The dataset was randomly split into 70% for training, 15% for validation, and 15% for testing.
中文视频搜索引擎的用户Query，包括26个Query分类类别，数据集划分同BSM。
A dataset of 150K search queries was collected from publicly accessible video websites, classified into 26 fine-grained categories such as film, travel, music, fashion, and sports. The data is split into 70% for training, 15% for validation, and 15% for testing, consistent with the BSM dataset.
Note that 1K is equal to 1000.
| Dataset | #Class | Avg.Len | Size(K) | #Words | #Train | #Val | #Test |
|---|---|---|---|---|---|---|---|
| MR[1] | 2 | 21.0 | 10.6 | 21,384 | 7,464 | 1,599 | 1,599 |
| SST-1 | 5 | 18.4 | 11.8 | 17,836 | 8,544 | 2,210 | 1,101 |
| SST-2 | 2 | 18.5 | 9.6 | 16,188 | 6,920 | 872 | 1,821 |
| SUBJ [2] | 2 | 23.3 | 10 | 21,301 | 7,001 | 1,495 | 1,499 |
| TREC [3] | 6 | 9.8 | 6.4 | 8,764 | 5,452 | 500 | 500 |
| CR [4] | 2 | 18.8 | 3.7 | 5,339 | 2,639 | 566 | 565 |
| MPQA [5] | 2 | 3.1 | 10.6 | 6,246 | 7,423 | 1,590 | 1,590 |
| Snippets [6] | 8 | 14.5 | 12.3 | 29,040 | 8,368 | 1,851 | 1,851 |
| Ohsumed [7] | 23 | 6.8 | 7.4 | 11,764 | 5,180 | 1,110 | 1,110 |
| 2 | 3.5 | 10 | 21,065 | 7,000 | 1,500 | 1,500 | |
| TagMyNews [8] | 7 | 5.1 | 32.5 | 38,629 | 22,785 | 4,882 | 4,882 |
| DanMU(or BSM)[9,10] | 2 | 6.3 | 100 | 4,560 | 70K | 15K | 15K |
| CVQD(or VSQ)[9,10] | 26 | 8.5 | 150 | 5,320 | 105K | 22.5K | 22.5K |
cd pathwalk
nohup bash run_training.sh > r.log 2>&1 &
@Inproceedings{TermGraph,
author = {Wei Pang and Duan Ruixue and Ning Li},
title = {Within-Dataset Graph Enhancement for Short Text Classification},
booktitle = {ECMLPKDD Workshop},
year = {2022},
pages = {},
url = {https://openreview.net/forum?id=KOZTylWrOYu&referrer=%5Bthe%20profile%20of%20Wei%20Pang%5D(%2Fprofile%3Fid%3D~Wei_Pang4)},
}
@Article{ShortGraph,
author = {Wei Pang},
title = {Short Text Classification via Term Graph},
journal = {arXiv.2001.10338},
month = {Jan},
year = {2020},
pages = {},
doi = {https://doi.org/10.48550/arXiv.2001.10338}
}
[1] B. Pang, L. Lee, Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, in: ACL, 2005, pp. 115–124
[2] B. Pang, L. Lee, A sentimental education: Sentiment analysis using sub- jectivity summarization based on minimum cuts, in: ACL, 2004.
[3] X. Li, D. Roth, Learning question classifiers, in: ACL, 2002
[4] M. Hu, B. Liu, Mining and summarizing customer reviews, in: KDD, 2004
[5] L. Deng, J. Wiebe, Mpqa 3.0: Entity/event-level sentiment corpus, in: NAACL, 2015, pp. 1323–1328
[6] X.-H. Phan, L.-M. Nguyen, S. Horiguchi, Learning to classify short and sparse text and web with hidden topics from large-scale data collections, in: WWW, 2008, pp. 91–100
[7] W. Hersh, C. Buckley, T. Leone, D. Hickam, Ohsumed: An interactive retrieval evaluation and new large test collection for research, in: SIGIR, 1994, pp. 192–201
[8] D. Vitale, P. Ferragina, U. Scaiella, Classification of short texts by deploying topical annotations, in: ECIR, 2012, pp. 376–387
[9] W. Pang et al., Within-Dataset Graph Enhancement for Short Text Classification, in ECML-PKDD 2022
[10] Wei Pang. Short Text Classification via Term Graph, in arXiv:2001.10338, 2020
All datasets are licensed under Apache 2.0.