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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 72285

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Special Issue Editors

Prof. Dr. Tim Schlippe

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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

Prof. Dr. Matthias Wölfel

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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

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Published Papers (13 papers)

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Research

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16 pages, 1756 KiB

Open AccessArticle

Practical Design and Implementation of Virtual Chatbot Assistants for Bioinformatics Based on a NLU Open Framework

by Aya Allah Elsayed, Ahmed Ibrahem Hafez, Raquel Ceprián, Genís Martínez, Alejandro Granados, Beatriz Soriano, Carlos Llorens and José M. Sempere

Big Data Cogn. Comput. 2024, 8(11), 163; https://doi.org/10.3390/bdcc8110163 - 20 Nov 2024

Viewed by 351

Abstract

In this work, we describe the implementation of an infrastructure of conversational chatbots by using natural language processing and training within the Rasa framework. We use this infrastructure to create a chatbot assistant for the users of a bioinformatics suite. This suite provides a customized interface solution for omic pipelines and workflows, and it is named GPRO. The infrastructure has also been used to build another chatbot for a Laboratory Information Management System (LIMS). The two chatbots (namely, Genie and Abu) have been built on an open framework that uses natural language understanding (NLU) and machine learning techniques to understand user queries and respond to them. Users can seamlessly interact with the chatbot to receive support on navigating the GPRO pipelines and workflows. The chatbot provides a bridge between users and the wealth of bioinformatics knowledge available online. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)

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25 pages, 1115 KiB

Open AccessArticle

Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages

by Koena Ronny Mabokela, Mpho Primus and Turgay Celik

Big Data Cogn. Comput. 2024, 8(11), 160; https://doi.org/10.3390/bdcc8110160 - 15 Nov 2024

Viewed by 353

Abstract

Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represented in many African languages. While state-of-the-art Afrocentric pre-trained language models (PLMs) have been developed for various natural language processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain largely unexplored. In this study, we propose a novel approach that combines Afrocentric PLMs with XAI techniques for sentiment analysis. We demonstrate the effectiveness of incorporating attention mechanisms and visualization techniques in improving the transparency, trustworthiness, and decision-making capabilities of transformer-based models when making sentiment predictions. To validate our approach, we employ the SAfriSenti corpus, a multilingual sentiment dataset for South African under-resourced languages, and perform a series of sentiment analysis experiments. These experiments enable comprehensive evaluations, comparing the performance of Afrocentric models against mainstream PLMs. Our results show that the Afro-XLMR model outperforms all other models, achieving an average F1-score of 71.04% across five tested languages, and the lowest error rate among the evaluated models. Additionally, we enhance the interpretability and explainability of the Afro-XLMR model using Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These XAI techniques ensure that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in AI-driven NLP technologies, particularly in the context of African languages. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)

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28 pages, 2857 KiB

Open AccessArticle

IndoGovBERT: A Domain-Specific Language Model for Processing Indonesian Government SDG Documents

by Agus Riyadi, Mate Kovacs, Uwe Serdült and Victor Kryssanov

Big Data Cogn. Comput. 2024, 8(11), 153; https://doi.org/10.3390/bdcc8110153 - 9 Nov 2024

Viewed by 743

Abstract

Achieving the Sustainable Development Goals (SDGs) requires collaboration among various stakeholders, particularly governments and non-state actors (NSAs). This collaboration results in but is also based on a continually growing volume of documents that needs to be analyzed and processed in a systematic way by government officials. Artificial Intelligence and Natural Language Processing (NLP) could, thus, offer valuable support for progressing towards SDG targets, including automating the government budget tagging and classifying NSA requests and initiatives, as well as helping uncover the possibilities for matching these two categories of activities. Many non-English speaking countries, including Indonesia, however, face limited NLP resources, such as, for instance, domain-specific pre-trained language models (PTLMs). This circumstance makes it difficult to automate document processing and improve the efficacy of SDG-related government efforts. The presented study introduces IndoGovBERT, a Bidirectional Encoder Representations from Transformers (BERT)-based PTLM built with domain-specific corpora, leveraging the Indonesian government’s public and internal documents. The model is intended to automate various laborious tasks of SDG document processing by the Indonesian government. Different approaches to PTLM development known from the literature are examined in the context of typical government settings. The most effective, in terms of the resultant model performance, but also most efficient, in terms of the computational resources required, methodology is determined and deployed for the development of the IndoGovBERT model. The developed model is then scrutinized in several text classification and similarity assessment experiments, where it is compared with four Indonesian general-purpose language models, a non-transformer approach of the Multilabel Topic Model (MLTM), as well as with a Multilingual BERT model. Results obtained in all experiments highlight the superior capability of the IndoGovBERT model for Indonesian government SDG document processing. The latter suggests that the proposed PTLM development methodology could be adopted to build high-performance specialized PTLMs for governments around the globe which face SDG document processing and other NLP challenges similar to the ones dealt with in the presented study. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)

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13 pages, 1255 KiB

Open AccessArticle

International Classification of Diseases Prediction from MIMIIC-III Clinical Text Using Pre-Trained ClinicalBERT and NLP Deep Learning Models Achieving State of the Art

by Ilyas Aden, Christopher H. T. Child and Constantino Carlos Reyes-Aldasoro

Big Data Cogn. Comput. 2024, 8(5), 47; https://doi.org/10.3390/bdcc8050047 - 10 May 2024

Viewed by 1999

Abstract

The International Classification of Diseases (ICD) serves as a widely employed framework for assigning diagnosis codes to electronic health records of patients. These codes facilitate the encapsulation of diagnoses and procedures conducted during a patient’s hospitalisation. This study aims to devise a predictive model for ICD codes based on the MIMIC-III clinical text dataset. Leveraging natural language processing techniques and deep learning architectures, we constructed a pipeline to distill pertinent information from the MIMIC-III dataset: the Medical Information Mart for Intensive Care III (MIMIC-III), a sizable, de-identified, and publicly accessible repository of medical records. Our method entails predicting diagnosis codes from unstructured data, such as discharge summaries and notes encompassing symptoms. We used state-of-the-art deep learning algorithms, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, bidirectional LSTM (BiLSTM) and BERT models after tokenizing the clinical test with Bio-ClinicalBERT, a pre-trained model from Hugging Face. To evaluate the efficacy of our approach, we conducted experiments utilizing the discharge dataset within MIMIC-III. Employing the BERT model, our methodology exhibited commendable accuracy in predicting the top 10 and top 50 diagnosis codes within the MIMIC-III dataset, achieving average accuracies of 88% and 80%, respectively. In comparison to recent studies by Biseda and Kerang, as well as Gangavarapu, which reported F1 scores of 0.72 in predicting the top 10 ICD-10 codes, our model demonstrated better performance, with an F1 score of 0.87. Similarly, in predicting the top 50 ICD-10 codes, previous research achieved an F1 score of 0.75, whereas our method attained an F1 score of 0.81. These results underscore the better performance of deep learning models over conventional machine learning approaches in this domain, thus validating our findings. The ability to predict diagnoses early from clinical notes holds promise in assisting doctors or physicians in determining effective treatments, thereby reshaping the conventional paradigm of diagnosis-then-treatment care. Our code is available online. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)

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12 pages, 273 KiB

Open AccessArticle

Knowledge-Enhanced Prompt Learning for Few-Shot Text Classification

by Jinshuo Liu and Lu Yang

Big Data Cogn. Comput. 2024, 8(4), 43; https://doi.org/10.3390/bdcc8040043 - 18 Apr 2024

Cited by 1 | Viewed by 1982

Abstract

Classification methods based on fine-tuning pre-trained language models often require a large number of labeled samples; therefore, few-shot text classification has attracted considerable attention. Prompt learning is an effective method for addressing few-shot text classification tasks in low-resource settings. The essence of prompt tuning is to insert tokens into the input, thereby converting a text classification task into a masked language modeling problem. However, constructing appropriate prompt templates and verbalizers remains challenging, as manual prompts often require expert knowledge, while auto-constructing prompts is time-consuming. In addition, the extensive knowledge contained in entities and relations should not be ignored. To address these issues, we propose a structured knowledge prompt tuning (SKPT) method, which is a knowledge-enhanced prompt tuning approach. Specifically, SKPT includes three components: prompt template, prompt verbalizer, and training strategies. First, we insert virtual tokens into the prompt template based on open triples to introduce external knowledge. Second, we use an improved knowledgeable verbalizer to expand and filter the label words. Finally, we use structured knowledge constraints during the training phase to optimize the model. Through extensive experiments on few-shot text classification tasks with different settings, the effectiveness of our model has been demonstrated. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Language Processing)

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24 pages, 2186 KiB

Open AccessArticle

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 2076

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 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

Open AccessArticle

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 6 | Viewed by 3830

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 2180

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 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

Open AccessArticle

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 5 | Viewed by 3067

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, 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

Open AccessArticle

Twi Machine Translation

by Frederick Gyasi and Tim Schlippe

Big Data Cogn. Comput. 2023, 7(2), 114; https://doi.org/10.3390/bdcc7020114 - 8 Jun 2023

Cited by 5 | Viewed by 3294

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 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

Open AccessEditor’s ChoiceArticle

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 13 | Viewed by 6502

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, 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

Open AccessEditor’s ChoiceArticle

“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 241 | Viewed by 37510

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 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

Open AccessReview

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

Cited by 4 | Viewed by 5214

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 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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