  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.
