Artificial Intelligence and Natural Language Processing

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 45519

Special Issue Editors


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Guest Editor
Professor of Artificial Intelligence, IU International University of Applied Sciences, 99084 Erfurt, Germany
Interests: natural language processing; AI for social good; AI in education; multilingual speech and language processing

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Guest Editor
Computer Science and Business Information Systems, Karlsruhe University of Applied Science, 76137 Karlsruhe, Germany
Interests: interaction design; human–computer interaction; artificial intelligence; augmented reality; virtual reality; digital culture; automatic speech processing and language understanding

Special Issue Information

Dear Colleagues,

Both society and numerous companies are currently facing enormous changes due to the rapid advance of machine learning and artificial intelligence (AI). Many decisions that accompany us at work and in everyday life are already supported or automated by AI.

With increasingly powerful natural language processing (NLP) models, the field of NLP has become extremely popular—both in the private and the business sector. There are a large number of applications and use cases where NLP offers great support to people. Popular applications are, e.g., voice assistants, chatbots, machine translation, and sentiment analysis.

However, there are still many challenges for AI-driven NLP applications like under-resourced languages or the topic of explainable AI and currently, there are many efforts to use NLP for social good and education.

This Special Issue is aimed at bringing together contributions from different disciplines dealing with Artificial Intelligence and Natural Language Processing not only to understand state-of-the-art techniques but also to address the aforementioned challenges, design new use cases, and build new applications which help humanity.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Applications and Use Cases in the Field of NLP
  • NLP for Low-Resource Languages
  • Multilingual Speech and Language Processing
  • Visualization in NLP
  • Explainable AI in NLP Applications
  • Deep Learning and Transformer-based Approaches for NLP
  • Sentiment Analysis
  • Question Answering
  • Text Simplification
  • Machine Translation
  • Topic Modeling
  • Language Modeling
  • NLP in Education
  • Automatic Grading
  • AI-based Tutoring Systems
  • Natural Language-based Recommender Systems
  • NLP for Social Good
  • NLP to Detect and Reduce Bias
  • NLP to Improve Lives and Mental Health
  • NLP for Political Decision-Making and Human Rights
  • NLP for Climate Change or Disaster Response
  • NLP to Analyze Media Manipulation, Fake News and Misinformation
  • NLP for Gender/Demographical Equality
  • NLP to Prevent Future Scandals of Conversational Bots

We look forward to receiving your contributions.

Prof. Dr. Tim Schlippe
Prof. Dr. Matthias Wölfel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • natural language processing
  • artificial intelligence
  • AI for social good
  • AI in education
  • low-resource languages
  • multilingual speech and language processing
  • sentiment analysis
  • question answering
  • text simplification

Published Papers (8 papers)

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Research

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24 pages, 2186 KiB  
Article
A Machine Learning-Based Pipeline for the Extraction of Insights from Customer Reviews
by Róbert Lakatos, Gergő Bogacsovics, Balázs Harangi, István Lakatos, Attila Tiba, János Tóth, Marianna Szabó and András Hajdu
Big Data Cogn. Comput. 2024, 8(3), 20; https://doi.org/10.3390/bdcc8030020 - 22 Feb 2024
Viewed by 1064
Abstract
The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews [...] Read more.
The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clustering. The elements of our model have been integrated and tailored to better meet the requirements of efficient information extraction and topic modeling of the extracted information for opinion mining. Our approach was validated and compared with other state-of-the-art methods using publicly available benchmark datasets. The results show that our system performs better than existing topic modeling and keyword extraction methods in this task. Full article
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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20 pages, 724 KiB  
Article
Knowledge-Based and Generative-AI-Driven Pedagogical Conversational Agents: A Comparative Study of Grice’s Cooperative Principles and Trust
by Matthias Wölfel, Mehrnoush Barani Shirzad, Andreas Reich and Katharina Anderer
Big Data Cogn. Comput. 2024, 8(1), 2; https://doi.org/10.3390/bdcc8010002 - 26 Dec 2023
Cited by 1 | Viewed by 2171
Abstract
The emergence of generative language models (GLMs), such as OpenAI’s ChatGPT, is changing the way we communicate with computers and has a major impact on the educational landscape. While GLMs have great potential to support education, their use is not unproblematic, as they [...] Read more.
The emergence of generative language models (GLMs), such as OpenAI’s ChatGPT, is changing the way we communicate with computers and has a major impact on the educational landscape. While GLMs have great potential to support education, their use is not unproblematic, as they suffer from hallucinations and misinformation. In this paper, we investigate how a very limited amount of domain-specific data, from lecture slides and transcripts, can be used to build knowledge-based and generative educational chatbots. We found that knowledge-based chatbots allow full control over the system’s response but lack the verbosity and flexibility of GLMs. The answers provided by GLMs are more trustworthy and offer greater flexibility, but their correctness cannot be guaranteed. Adapting GLMs to domain-specific data trades flexibility for correctness. Full article
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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12 pages, 540 KiB  
Article
An Artificial-Intelligence-Driven Spanish Poetry Classification Framework
by Shutian Deng, Gang Wang, Hongjun Wang and Fuliang Chang
Big Data Cogn. Comput. 2023, 7(4), 183; https://doi.org/10.3390/bdcc7040183 - 14 Dec 2023
Viewed by 1535
Abstract
Spain possesses a vast number of poems. Most have features that mean they present significantly different styles. A superficial reading of these poems may confuse readers due to their complexity. Therefore, it is of vital importance to classify the style of the poems [...] Read more.
Spain possesses a vast number of poems. Most have features that mean they present significantly different styles. A superficial reading of these poems may confuse readers due to their complexity. Therefore, it is of vital importance to classify the style of the poems in advance. Currently, poetry classification studies are mostly carried out manually, which creates extremely high requirements for the professional quality of classifiers and consumes a large amount of time. Furthermore, the objectivity of the classification cannot be guaranteed because of the influence of the classifier’s subjectivity. To solve these problems, a Spanish poetry classification framework was designed using artificial intelligence technology, which improves the accuracy, efficiency, and objectivity of classification. First, an artificial-intelligence-driven Spanish poetry classification framework is described in detail, and is illustrated by a framework diagram to clearly represent each step in the process. The framework includes many algorithms and models, such as the Term Frequency–Inverse Document Frequency (TF_IDF), Bagging, Support Vector Machines (SVMs), Adaptive Boosting (AdaBoost), logistic regression (LR), Gradient Boosting Decision Trees (GBDT), LightGBM (LGB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The roles of each algorithm in the framework are clearly defined. Finally, experiments were performed for model selection, comparing the results of these algorithms.The Bagging model stood out for its high accuracy, and the experimental results showed that the proposed framework can help researchers carry out poetry research work more efficiently, accurately, and objectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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14 pages, 1737 KiB  
Article
Empowering Short Answer Grading: Integrating Transformer-Based Embeddings and BI-LSTM Network
by Wael H. Gomaa, Abdelrahman E. Nagib, Mostafa M. Saeed, Abdulmohsen Algarni and Emad Nabil
Big Data Cogn. Comput. 2023, 7(3), 122; https://doi.org/10.3390/bdcc7030122 - 21 Jun 2023
Cited by 1 | Viewed by 2105
Abstract
Automated scoring systems have been revolutionized by natural language processing, enabling the evaluation of students’ diverse answers across various academic disciplines. However, this presents a challenge as students’ responses may vary significantly in terms of length, structure, and content. To tackle this challenge, [...] Read more.
Automated scoring systems have been revolutionized by natural language processing, enabling the evaluation of students’ diverse answers across various academic disciplines. However, this presents a challenge as students’ responses may vary significantly in terms of length, structure, and content. To tackle this challenge, this research introduces a novel automated model for short answer grading. The proposed model uses pretrained “transformer” models, specifically T5, in conjunction with a BI-LSTM architecture which is effective in processing sequential data by considering the past and future context. This research evaluated several preprocessing techniques and different hyperparameters to identify the most efficient architecture. Experiments were conducted using a standard benchmark dataset named the North Texas Dataset. This research achieved a state-of-the-art correlation value of 92.5 percent. The proposed model’s accuracy has significant implications for education as it has the potential to save educators considerable time and effort, while providing a reliable and fair evaluation for students, ultimately leading to improved learning outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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12 pages, 416 KiB  
Article
Twi Machine Translation
by Frederick Gyasi and Tim Schlippe
Big Data Cogn. Comput. 2023, 7(2), 114; https://doi.org/10.3390/bdcc7020114 - 08 Jun 2023
Cited by 1 | Viewed by 2185
Abstract
French is a strategically and economically important language in the regions where the African language Twi is spoken. However, only a very small proportion of Twi speakers in Ghana speak French. The development of a Twi–French parallel corpus and corresponding machine translation applications [...] Read more.
French is a strategically and economically important language in the regions where the African language Twi is spoken. However, only a very small proportion of Twi speakers in Ghana speak French. The development of a Twi–French parallel corpus and corresponding machine translation applications would provide various advantages, including stimulating trade and job creation, supporting the Ghanaian diaspora in French-speaking nations, assisting French-speaking tourists and immigrants seeking medical care in Ghana, and facilitating numerous downstream natural language processing tasks. Since there are hardly any machine translation systems or parallel corpora between Twi and French that cover a modern and versatile vocabulary, our goal was to extend a modern Twi–English corpus with French and develop machine translation systems between Twi and French: Consequently, in this paper, we present our Twi–French corpus of 10,708 parallel sentences. Furthermore, we describe our machine translation experiments with this corpus. We investigated direct machine translation and cascading systems that use English as a pivot language. Our best Twi–French system is a direct state-of-the-art transformer-based machine translation system that achieves a BLEU score of 0.76. Our best French–Twi system, which is a cascading system that uses English as a pivot language, results in a BLEU score of 0.81. Both systems are fine tuned with our corpus, and our French–Twi system even slightly outperforms Google Translate on our test set by 7% relative. Full article
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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33 pages, 12116 KiB  
Article
MalBERTv2: Code Aware BERT-Based Model for Malware Identification
by Abir Rahali and Moulay A. Akhloufi
Big Data Cogn. Comput. 2023, 7(2), 60; https://doi.org/10.3390/bdcc7020060 - 24 Mar 2023
Cited by 8 | Viewed by 4366
Abstract
To proactively mitigate malware threats, cybersecurity tools, such as anti-virus and anti-malware software, as well as firewalls, require frequent updates and proactive implementation. However, processing the vast amounts of dataset examples can be overwhelming when relying solely on traditional methods. In cybersecurity workflows, [...] Read more.
To proactively mitigate malware threats, cybersecurity tools, such as anti-virus and anti-malware software, as well as firewalls, require frequent updates and proactive implementation. However, processing the vast amounts of dataset examples can be overwhelming when relying solely on traditional methods. In cybersecurity workflows, recent advances in natural language processing (NLP) models can aid in proactively detecting various threats. In this paper, we present a novel approach for representing the relevance and significance of the Malware/Goodware (MG) datasets, through the use of a pre-trained language model called MalBERTv2. Our model is trained on publicly available datasets, with a focus on the source code of the apps by extracting the top-ranked files that present the most relevant information. These files are then passed through a pre-tokenization feature generator, and the resulting keywords are used to train the tokenizer from scratch. Finally, we apply a classifier using bidirectional encoder representations from transformers (BERT) as a layer within the model pipeline. The performance of our model is evaluated on different datasets, achieving a weighted f1 score ranging from 82% to 99%. Our results demonstrate the effectiveness of our approach for proactively detecting malware threats using NLP techniques. Full article
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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10 pages, 1754 KiB  
Article
“What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter
by Viriya Taecharungroj
Big Data Cogn. Comput. 2023, 7(1), 35; https://doi.org/10.3390/bdcc7010035 - 16 Feb 2023
Cited by 124 | Viewed by 29486
Abstract
In this study, the author collected tweets about ChatGPT, an innovative AI chatbot, in the first month after its launch. A total of 233,914 English tweets were analyzed using the latent Dirichlet allocation (LDA) topic modeling algorithm to answer the question “what can [...] Read more.
In this study, the author collected tweets about ChatGPT, an innovative AI chatbot, in the first month after its launch. A total of 233,914 English tweets were analyzed using the latent Dirichlet allocation (LDA) topic modeling algorithm to answer the question “what can ChatGPT do?”. The results revealed three general topics: news, technology, and reactions. The author also identified five functional domains: creative writing, essay writing, prompt writing, code writing, and answering questions. The analysis also found that ChatGPT has the potential to impact technologies and humans in both positive and negative ways. In conclusion, the author outlines four key issues that need to be addressed as a result of this AI advancement: the evolution of jobs, a new technological landscape, the quest for artificial general intelligence, and the progress-ethics conundrum. Full article
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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Review

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28 pages, 718 KiB  
Review
From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions
by Tamim Mahmud Al-Hasan, Aya Nabil Sayed, Faycal Bensaali, Yassine Himeur, Iraklis Varlamis and George Dimitrakopoulos
Big Data Cogn. Comput. 2024, 8(4), 36; https://doi.org/10.3390/bdcc8040036 - 27 Mar 2024
Viewed by 785
Abstract
Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data [...] Read more.
Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data sparsity problem. This survey paper presents an in-depth analysis of the paradigm shift from conventional recommender systems to generative pre-trained-transformers-(GPT)-based chatbots. We highlight recent developments that leverage the power of GPT to create interactive and personalized conversational agents. By exploring natural language processing (NLP) and deep learning techniques, we investigate how GPT models can better understand user preferences and provide context-aware recommendations. The paper further evaluates the advantages and limitations of GPT-based recommender systems, comparing their performance with traditional methods. Additionally, we discuss potential future directions, including the role of reinforcement learning in refining the personalization aspect of these systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)
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