Regularizing Text Categorization with Clusters of Words

By Konstantinos Skianis, François Rousseau, Michalis Vazirgiannis. 2016

15 citations recorded by the source corpus.

Regularization is a critical step in supervised learning to not only address overfitting, but also to take into account any prior knowledge we may have on the features and their dependence. In this paper, we explore stateof-the-art structured regularizers and we propose novel ones based on clusters of words from LSI topics, word2vec embeddings and graph-of-words document representation. We show that our proposed regularizers are faster than the state-of-the-art ones and still improve text classification accuracy. Code and data are available online1.

Subfields: Artificial Intelligence, 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. Liang Yao
2. Chengsheng Mao
3. Yuan Luo
4. Erik Cambria
5. Philip S. Yu
6. Shirui Pan
7. Jiawei Zhang
8. Haopeng Zhang

In Natural Language Processing

1. Liang Yao
2. Chengsheng Mao
3. Yuan Luo
4. Jiawei Zhang
5. Haopeng Zhang
6. Hao Zhu
7. Piotr Koniusz
8. Maarten de Rijke

Citations in the corpus

• Graph Convolutional Networks for Text Classification (2018, methodology): “(Tai, Socher, and Manning 2015), (Liu, Qiu, and Huang 2016) and (Luo 2017) used LSTM, a specific type of RNN, to learn text representation.”