# Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

arXiv: https://arxiv.org/abs/1609.00718 · HTML: https://mlsanity.com/paper/1609.00718/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [Deep Learning-based Sentiment Classification: A Comparative Survey](https://mlsanity.com/paper/2312.17253/) (2023)
- [Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification](https://mlsanity.com/paper/2303.09115/) (2023)

## Abstract (arXiv)

This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in Conneau et al., though the results should be interpreted with some consideration due to the unique pre-processing of Conneau et al. The shallow word-level CNN uses more parameters and therefore requires more storage than the deep character-level CNN; however, the shallow word-level CNN computes much faster.