Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence
Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …
powerful modeling capabilities and have been successfully applied in natural language …
Review of graph neural network in text classification
M Malekzadeh, P Hajibabaee, M Heidari… - 2021 IEEE 12th …, 2021 - ieeexplore.ieee.org
Text classification is one of the fundamental problems in Natural Language Processing
(NLP). Several research studies have used deep learning approaches such as Convolution …
(NLP). Several research studies have used deep learning approaches such as Convolution …
A survey on text classification: From traditional to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
A survey on text classification: From shallow to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Balancing methods for multi-label text classification with long-tailed class distribution
Multi-label text classification is a challenging task because it requires capturing label
dependencies. It becomes even more challenging when class distribution is long-tailed …
dependencies. It becomes even more challenging when class distribution is long-tailed …
Effective convolutional attention network for multi-label clinical document classification
Multi-label document classification (MLDC) problems can be challenging, especially for long
documents with a large label set and a long-tail distribution over labels. In this paper, we …
documents with a large label set and a long-tail distribution over labels. In this paper, we …
Large scale legal text classification using transformer models
Large multi-label text classification is a challenging Natural Language Processing (NLP)
problem that is concerned with text classification for datasets with thousands of labels. We …
problem that is concerned with text classification for datasets with thousands of labels. We …
Well-calibrated confidence measures for multi-label text classification with a large number of labels
We extend our previous work on Inductive Conformal Prediction (ICP) for multi-label text
classification and present a novel approach for addressing the computational inefficiency of …
classification and present a novel approach for addressing the computational inefficiency of …
SHO-CNN: A metaheuristic optimization of a convolutional neural network for multi-label news classification
News media always pursue informing the public at large. It is impossible to overestimate the
significance of understanding the semantics of news coverage. Traditionally, a news text is …
significance of understanding the semantics of news coverage. Traditionally, a news text is …
A survey of text classification with transformers: How wide? how large? how long? how accurate? how expensive? how safe?
J Fields, K Chovanec, P Madiraju - IEEE Access, 2024 - ieeexplore.ieee.org
Text classification in natural language processing (NLP) is evolving rapidly, particularly with
the surge in transformer-based models, including large language models (LLM). This paper …
the surge in transformer-based models, including large language models (LLM). This paper …