# Text Classification using Capsules

Source: [Text Classification using Capsules](<https://www.sota2.com/research/paper/68fa64bf1e209131cea0640f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:10:58.701Z

Authors: Jaeyoung Kim, Sion Jang, Sungchul Choi, Eunjeong Park

Published: 2018-08-12T18:08:38.000Z

## Abstract

This paper presents an empirical exploration of the use of capsule networks for text classification. While it has been shown that capsule networks are effective for image classification, their validity in the domain of text has not been explored. In this paper, we show that capsule networks indeed have the potential for text classification and that they have several advantages over convolutional neural networks. We further suggest a simple routing method that effectively reduces the computational complexity of dynamic routing. We utilized seven benchmark datasets to demonstrate that capsule networks, along with the proposed routing method provide comparable results.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1808.03976>)

[PDF](<https://arxiv.org/pdf/1808.03976.pdf>)