# Classifying textual data: shallow, deep and ensemble methods

arXiv: https://arxiv.org/abs/1902.07068 · HTML: https://mlsanity.com/paper/1902.07068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Guarded Query Routing for Large Language Models](https://mlsanity.com/paper/2505.14524/) (2025)

## Abstract (arXiv)

This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods. The study is motivated by a challenging real data problem, characterized by high-dimensional and extremely sparse data, deriving from incoming calls to the customer care of an Italian phone company. We will show that deep learning outperforms many classical (shallow) strategies but the combination of shallow and deep learning methods in a unique ensemble classifier may improve the robustness and the accuracy of "single" classification methods.