Document classification by topic labeling

By Swapnil Hingmire, Sandeep Chougule, Girish Keshav Palshikar, Sutanu Chakraborti. 2013

82 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

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. Kang Liu
2. Jun Zhao
3. Dat Quoc Nguyen
4. Mark Johnson
5. Lan Du
6. Richard Billingsley
7. Siwei Lai
8. Liheng Xu

In Computer Vision and Pattern Recognition

1. Jun Zhao
2. Kang Liu
3. Mark Johnson
4. Lan Du
5. Dat Quoc Nguyen
6. Liheng Xu
7. Mohit Bansal
8. Dacheng Tao

In Information Systems

1. Jun Zhao
2. Mark Johnson
3. Lan Du
4. Kang Liu
5. Richard Billingsley
6. Dat Quoc Nguyen

Citations in the corpus

• Recurrent Convolutional Neural Networks for Text Classification (2015, methodology): “Furthermore, several feature selection methods, such as frequency, MI (Cover and Thomas 2012), pLSA (Cai and Hofmann 2003), LDA (Hingmire et al. 2013), are applied to select more discriminative features.”
• Improving Topic Models with Latent Feature Word Representations (2015, methodology): “, 2003) and related methods (Blei, 2012), are often used to learn a set of latent topics for a corpus, and predict the probabilities of each word in each document belonging to each topic (Teh et al., 2006; Newman et al., 2006; Toutanova and Johnson, 2008; Porteous et al., 2008; Johnson, 2010; Xie an”