applied

sciences
Editorial
Special Issue “Recent Trends in Natural Language Processing
and Its Applications”
Paolo Mengoni 1, * and Valentino Santucci 2

1 School of Communication, Hong Kong Baptist University, 224 Waterloo Rd., Kowloon Tong,
Hong Kong 999077, China
2 Department of Humanities and Social Sciences, University for Foreigners of Perugia, 06123 Perugia, Italy;
valentino.santucci@unistrapg.it
* Correspondence: pmengoni@hkbu.edu.hk

The recent advancements in Artificial Intelligence have paved the way for remark-
able achievements in tasks that have traditionally posed challenges even for humans.
One of the notable applications is Natural Language Processing (NLP), which has re-
cently gained prominence across various fields for tackling important tasks such as
machine translation, natural language understanding, question answering, fake news
detection, and more. Despite these accomplishments, the NLP field still faces signifi-
cant challenges that necessitate the development of novel techniques and approaches.
One example is the adaptation of groundbreaking NLP methods originally devised for
English to other languages.
This Special Issue focused on recent trends and original applications of NLP. The
state-of-the-art pieces of work published in this Special Issue delve into a range of topics,
including sentiment analysis, information retrieval, natural language understanding, and
applications to low-resource natural languages.
A total of eleven articles are presented in this Special Issue.
Huang et al. [1] introduced a text classification model that combines an improved
self-attention mechanism with a skip-gate recurrent unit network to classify the irrelevant
words in text classification. Bombini et al. [2] proposed a cloud-native web application for
assisted metadata generation and retrieval based on a deep neural network for named entity
recognition. Arabic language was investigated by Boulouard et al. [3] to detect hateful and
Citation: Mengoni, P.; Santucci, V. offensive speech on Arabic websites and social media platforms using a transfer learning
Special Issue “Recent Trends in solution and by Alqurashi [4] to identify fine-grained Arabic language dialects in the form
Natural Language Processing and Its of short written text using several classical machine learning methods and deep learning
Applications”. Appl. Sci. 2023, 13, convolutional neural networks. Ahmed et al. [5] introduced a heuristic approach to increase
7284. https://doi.org/10.3390/
the accuracy of stacked autoencoders in sentiment analysis. Qin and Ronchieri [6] explored
app13127284
the effects of pandemic on social media posts by applying topic modeling and sentiment
Received: 7 June 2023 analysis to extract people’s concerns and attitudes regarding the pandemic. Li et al. [7]
Accepted: 13 June 2023 also used topic modeling together with bidirectional LSTM to improve the marketing
Published: 19 June 2023 effectiveness using the reviews of product short videos. Urdu language was analyzed in the
works of Li et al. [8] and Mehmood et al. [9]. The former work explores sentiment analysis
for the Roman Urdu language using transfer learning technique, while the latter introduces
a classification technique for threatening content on social media. Alashban et al. [10]
Copyright: © 2023 by the authors.
used a convolutional recurrent neural network for spoken language identification on seven
Licensee MDPI, Basel, Switzerland.
languages including Arabic. Finally, Alshahrani et al. [11] proposed a solution based on
This article is an open access article
deep learning for intent detection, a critical task in natural language understanding.
distributed under the terms and
Submissions for this Special Issue are now closed. Further studies and applica-
conditions of the Creative Commons
tions of NLP approaches continue to be proposed and address challenges that arise in
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
low-resource languages.
4.0/).

Appl. Sci. 2023, 13, 7284. https://doi.org/10.3390/app13127284 https://www.mdpi.com/journal/applsci

Author Contributions: All authors have contributed equally to this work. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments: Thank you to all the authors and peer reviewers for their contributions to this
Special Issue. We would also like to thank the MDPI staff for their support in bringing this Special
Issue to fruition.
Conflicts of Interest: The authors declares no conflict of interest.

References
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Recurrent Neural Network. Appl. Sci. 2022, 12, 9181. [CrossRef]
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Computational Linguistics with Deep-Learning-Based Intent Detection for Natural Language Understanding. Appl. Sci. 2022,
12, 8633. [CrossRef]

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