Automatic online news issue construction in web environment

By Canhui Wang, Min Zhang, Shaoping Ma, Liyun Ru. 2008

117 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ting-Wei Liu
2. Weinan Zhang
3. Chenliang Li
4. Kyunghyun Cho
5. Yann LeCun
6. W. Zhang
7. Quoc V. Le
8. Jianfeng Gao

In Information Systems

1. Yann LeCun
2. Weinan Zhang
3. Chenliang Li
4. Ting-Wei Liu
5. Junbo Jake Zhao

In Natural Language Processing

1. Ting-Wei Liu
2. Weinan Zhang
3. Yann LeCun
4. Chenliang Li
5. W. Zhang
6. Quan.Z Sheng
7. Ahoud Abdulrahmn F. Alhazmi
8. Junbo Jake Zhao

Citations in the corpus

• Character-level Convolutional Networks for Text Classification (2015, methodology): “This dataset is a combination of the SogouCA and SogouCS news corpora [32], containing in total 2,909,551 news articles in various topic channels.”
• How to Fine-Tune BERT for Text Classification? (2019, neutral): “The dataset is a combination of the So-gouCA and SogouCS news corpora (Wang et al., 2008).”
• A Survey on Text Classification: From Shallow to Deep Learning (2020, unknown): “Datasets C L N Related Papers Sources Applications MR 2 20 10,662 [18] [7] [78] [6] [174] SA SST-1 5 18 11,855 [33] [18] [2] [3][50] [175] SA SST-2 2 19 9,613 [33] [18] [8] [48] [19] [176] SA Subj 2 23 10,000 [18] [48] [78] [177] QA TREC 6 10 5,952 [18] [7] [8] [121] [178] QA CR 2 19 3,775 [18] [78]”
• Neural personalized response generation as domain adaptation (2017, methodology): “The word2vec is trained with the SogouCS&CA corpus (2008 version) 2 , which is widely used for Chinese text analysis [30, 31].”
• Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey (2019, methodology): “ed gender classification on and Twitter dataset11 for gender detection. [35] performed spam detection on Enron Spam Dataset [91] and adopted six large datasetsfrom[155], i.e., AG’s news12, Sogou news [138], DBPedia ontology dataset, Yahoo! Answers13 for text categorization and Yelp reviews14, Amazon”
• A Survey on Text Classification: From Traditional to Deep Learning (2020, unknown): “F 5 93 3,650,000 English [93, 108, 122] [232] SA Twitter 3 19 11,209 English [5][100] [233] SA NLP&CC 2013 2 - 115,606 Multi-language [110] [111] SA 20NG 20 221 18,846 English [72, 75, 106, 155, 157] [34] NC AG News 4 45/7 127,600 English [98, 100] [101, 138] [234] NC R8 8 66 7,674 English [155, 157”