# TextConvoNet:A Convolutional Neural Network based Architecture for Text Classification

arXiv: https://arxiv.org/abs/2203.05173 · HTML: https://mlsanity.com/paper/2203.05173/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 66 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Text Level Graph Neural Network for Text Classification](https://mlsanity.com/paper/1910.02356/) (2019)
- [A C-LSTM Neural Network for Text Classification](https://mlsanity.com/paper/1511.08630/) (2015)
- [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1058/) (2014)
- [Understanding Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/1809.08037/) (2018)
- [Do Convolutional Networks need to be Deep for Text Classification ?](https://mlsanity.com/paper/1707.04108/) (2017)
- [Multichannel CNN with Attention for Text Classification](https://mlsanity.com/paper/2006.16174/) (2020)
- [Squeezed Very Deep Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/1901.09821/) (2019)

## Built on by (most cited first, 5 of 5)

- [Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages](https://mlsanity.com/paper/2505.20496/) (2025)
- [Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain](https://mlsanity.com/paper/2407.10807/) (2024)
- [Overcoming Low-Resource Barriers in Tulu: Neural Models and Corpus Creation for OffensiveLanguage Identification](https://mlsanity.com/paper/2508.11166/) (2025)
- [Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space](https://mlsanity.com/paper/2602.23753/) (2026)
- [Audio Video Verbal Analysis (AVVA) for Capturing Classroom Dialogues](https://mlsanity.com/paper/2604.22043/) (2026)

## Abstract (arXiv)

In recent years, deep learning-based models have significantly improved the Natural Language Processing (NLP) tasks. Specifically, the Convolutional Neural Network (CNN), initially used for computer vision, has shown remarkable performance for text data in various NLP problems. Most of the existing CNN-based models use 1-dimensional convolving filters n-gram detectors), where each filter specialises in extracting n-grams features of a particular input word embedding. The input word embeddings, also called sentence matrix, is treated as a matrix where each row is a word vector. Thus, it allows the model to apply one-dimensional convolution and only extract n-gram based features from a sentence matrix. These features can be termed as intra-sentence n-gram features. To the extent of our knowledge, all the existing CNN models are based on the aforementioned concept. In this paper, we present a CNN-based architecture TextConvoNet that not only extracts the intra-sentence n-gram features but also captures the inter-sentence n-gram features in input text data. It uses an alternative approach for input matrix representation and applies a two-dimensional multi-scale convolutional operation on the input. To evaluate the performance of TextConvoNet, we perform an experimental study on five text classification datasets. The results are evaluated by using various performance metrics. The experimental results show that the presented TextConvoNet outperforms state-of-the-art machine learning and deep learning models for text classification purposes.