# Deep Neural Networks for Czech Multi-label Document Classification

arXiv: https://arxiv.org/abs/1701.03849 · HTML: https://mlsanity.com/paper/1701.03849/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [Czert -- Czech BERT-like Model for Language Representation](https://mlsanity.com/paper/2103.13031/) (2021)
- [Comparison of Czech Transformers on Text Classification Tasks](https://mlsanity.com/paper/2107.10042/) (2021)
- [Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels](https://mlsanity.com/paper/2312.09304/) (2023)

## Abstract (arXiv)

This paper is focused on automatic multi-label document classification of Czech text documents. The current approaches usually use some pre-processing which can have negative impact (loss of information, additional implementation work, etc). Therefore, we would like to omit it and use deep neural networks that learn from simple features. This choice was motivated by their successful usage in many other machine learning fields. Two different networks are compared: the first one is a standard multi-layer perceptron, while the second one is a popular convolutional network. The experiments on a Czech newspaper corpus show that both networks significantly outperform baseline method which uses a rich set of features with maximum entropy classifier. We have also shown that convolutional network gives the best results.