# In-depth Question classification using Convolutional Neural Networks

Source: [In-depth Question classification using Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa642c1e209131cea0320a>)

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

Data updated: 2025-10-25T20:58:11.540Z

Authors: Prudhvi Raj Dachapally, Srikanth Ramanam

Published: 2018-03-31T19:52:26.000Z

## Abstract

Convolutional neural networks for computer vision are fairly intuitive. In a typical CNN used in image classification, the first layers learn edges, and the following layers learn some filters that can identify an object. But CNNs for Natural Language Processing are not used often and are not completely intuitive. We have a good idea about what the convolution filters learn for the task of text classification, and to that, we propose a neural network structure that will be able to give good results in less time. We will be using convolutional neural networks to predict the primary or broader topic of a question, and then use separate networks for each of these predicted topics to accurately classify their sub-topics.

## 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/1804.00968>)

[PDF](<https://arxiv.org/pdf/1804.00968.pdf>)