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Applied Intelligence in Natural Language Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 31227

Special Issue Editors

Dr. Adriano L.I. Oliveira

E-Mail Website
Guest Editor
Centro de Informática, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
Interests: deep learning (artificial intelligence); feature extraction; Gaussian processes; convolutional neural nets; data handling; handwritten character recognition; image representation; learning (artificial intelligence); maximum entropy methods; mixture models; neural nets; auditing; biological techniques; biology computing; computerised instrumentation; data mining; diseases; electronic noses; feedforward neural nets; financial data processing; fraud; fuzzy neural nets; greedy algorithms; handwriting recognition; image classification
Prof. Dr. Ellen P.R. Souza

E-Mail Website
Guest Editor
Institute of Mathematics and Computing Sciences, Federal Rural University of Pernambuco, Recife 52171-900, Brazil
Interests: social network analysis; software testing; data mining and knowledge discovery; text mining

Special Issue Information

Dear Colleagues,

Nowadays, the explanation, formal modeling, and processing of language remain a challenge. In the past few years, the development of natural language processing has been able to deal with many issues such as emotional analysis, semantic analysis, and so on. One of the keys to the success of these real-life technologies is how NLP and AI integrate in seamless and appropriate ways.

This Special Issue aims to exploit the new opportunities of applied intelligence for future natural language processing by collecting new ideas, the latest findings, state-of-the-art results, and comprehensive surveys of applied intelligence. The topics in focus include, but are not limited to:

  • NLP for search, recommendation, and representation;
  • Natural language processing;
  • Artificial intelligence;
  • Text Analytics and Tourism;
  • Text annotation using deep-learning approaches;
  • Semantic reasoning;
  • DL-based techniques and tools for NLP.

Dr. Adriano L.I. Oliveira
Prof. Dr. Ellen P.R. Souza
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • sentiment analysis
  • information extraction
  • text summarization
  • machine translation
  • language modeling
  • speech recognition
  • text generation
  • text classification

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Published Papers (15 papers)

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Research

41 pages, 12999 KiB  
Article
The AI-Powered Evolution of Big Data
by Yulia Kumar, Jose Marchena, Ardalan H. Awlla, J. Jenny Li and Hemn Barzan Abdalla
Appl. Sci. 2024, 14(22), 10176; https://doi.org/10.3390/app142210176 - 6 Nov 2024
Viewed by 819
Abstract
The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded [...] Read more.
The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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18 pages, 4420 KiB  
Article
Machine Learning Approach for Arabic Handwritten Recognition
by A. M. Mutawa, Mohammad Y. Allaho and Monirah Al-Hajeri
Appl. Sci. 2024, 14(19), 9020; https://doi.org/10.3390/app14199020 - 6 Oct 2024
Viewed by 1103
Abstract
Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten [...] Read more.
Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. In the new model, the architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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23 pages, 7374 KiB  
Article
A Chinese Nested Named Entity Recognition Model for Chicken Disease Based on Multiple Fine-Grained Feature Fusion and Efficient Global Pointer
by Xiajun Wang, Cheng Peng, Qifeng Li, Qinyang Yu, Liqun Lin, Pingping Li, Ronghua Gao, Wenbiao Wu, Ruixiang Jiang, Ligen Yu, Luyu Ding and Lei Zhu
Appl. Sci. 2024, 14(18), 8495; https://doi.org/10.3390/app14188495 - 20 Sep 2024
Viewed by 3613
Abstract
Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a [...] Read more.
Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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13 pages, 328 KiB  
Article
Investigating Semantic Differences in User-Generated Content by Cross-Domain Sentiment Analysis Means
by Traian-Radu Ploscă, Christian-Daniel Curiac and Daniel-Ioan Curiac
Appl. Sci. 2024, 14(6), 2421; https://doi.org/10.3390/app14062421 - 13 Mar 2024
Viewed by 1091
Abstract
Sentiment analysis of domain-specific short messages (DSSMs) raises challenges due to their peculiar nature, which can often include field-specific terminology, jargon, and abbreviations. In this paper, we investigate the distinctive characteristics of user-generated content across multiple domains, with DSSMs serving as the central [...] Read more.
Sentiment analysis of domain-specific short messages (DSSMs) raises challenges due to their peculiar nature, which can often include field-specific terminology, jargon, and abbreviations. In this paper, we investigate the distinctive characteristics of user-generated content across multiple domains, with DSSMs serving as the central point. With cross-domain models on the rise, we examine the capability of the models to accurately interpret hidden meanings embedded in domain-specific terminology. For our investigation, we utilize three different community platform datasets: a Jira dataset for DSSMs as it contains particular vocabulary related to software engineering, a Twitter dataset for domain-independent short messages (DISMs) because it holds everyday speech type of language, and a Reddit dataset as an intermediary case. Through machine learning techniques, we thus explore whether software engineering short messages exhibit notable differences compared to regular messages. For this, we utilized the cross-domain knowledge transfer approach and RoBERTa sentiment analysis technique to prove the existence of efficient models in addressing DSSMs challenges across multiple domains. Our study reveals that DSSMs are semantically different from DISMs due to F1 score differences generated by the models. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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14 pages, 731 KiB  
Article
Link Prediction Based on Feature Mapping and Bi-Directional Convolution
by Ping Feng, Xin Zhang, Hang Wu, Yunyi Wang, Ziqian Yang and Dantong Ouyang
Appl. Sci. 2024, 14(5), 2089; https://doi.org/10.3390/app14052089 - 2 Mar 2024
Viewed by 1096
Abstract
A considerable amount of research on link prediction has recently been driven by missing relationships between knowledge graph entities and the problem of the incompleteness of knowledge graphs. Some recent studies have shown that convolutional neural networks based on knowledge embeddings are highly [...] Read more.
A considerable amount of research on link prediction has recently been driven by missing relationships between knowledge graph entities and the problem of the incompleteness of knowledge graphs. Some recent studies have shown that convolutional neural networks based on knowledge embeddings are highly expressive and have good performance in link prediction. However, we found that the convolutional neural network (CNN)-based models do not handle the link between relations and entities well. For this reason, this paper proposes a link prediction model (LPM) based on feature mapping and bi-directional convolution. For the modeling of the task, an encoding layer–mapping layer–decoding layer structure is used. Among these layers, the encoding layer adopts a graph attention network to encode multi-hop triad information and obtains richer encoding of entities and relationships. The mapping layer can realize the mapping transformation between entities and relations and project the entity encoding in the space of relation encoding to capture the subtle connection between entities and relations. The decoding layer adopts bidirectional convolution to merge and decode the triples in a sequential inverse order, which makes the decoding layer model more advantageous in prediction. In addition, the decoding layer also adopts the r-drop training method to effectively reduce the distribution error generated by training between models and enhance the robustness of the model. Our experiments demonstrated the effectiveness of mapping relations, bidirectional convolution, and r-drop, and the accuracy of the proposed model showed significant improvements for each evaluation metric on two datasets, WN18RR and FB15k-237. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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20 pages, 16529 KiB  
Article
Automated Text Annotation Using a Semi-Supervised Approach with Meta Vectorizer and Machine Learning Algorithms for Hate Speech Detection
by Shoffan Saifullah, Rafał Dreżewski, Felix Andika Dwiyanto, Agus Sasmito Aribowo, Yuli Fauziah and Nur Heri Cahyana
Appl. Sci. 2024, 14(3), 1078; https://doi.org/10.3390/app14031078 - 26 Jan 2024
Cited by 4 | Viewed by 2235
Abstract
Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has various drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. Therefore, we have developed the concept [...] Read more.
Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has various drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. Therefore, we have developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation to detect hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and new data of 2245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available (based on self-learning). Ultimately, this enhances its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, to 0.9. The experiments indicated that the DT-TF-IDF model has the best accuracy value of 97.1% with a scenario of 5%:80%:0.9. However, several other methods have accuracy above 90%, such as SVM (TF-IDF and Word2Vec) and KNN (Word2Vec), based on both text extraction methods in several test scenarios. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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24 pages, 4436 KiB  
Article
Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
by Samer Abdulateef Waheeb
Appl. Sci. 2024, 14(1), 300; https://doi.org/10.3390/app14010300 - 29 Dec 2023
Viewed by 1091
Abstract
This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid data sampling method that combines SMOTE, a [...] Read more.
This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid data sampling method that combines SMOTE, a meta-weigher with a meta-based self-training method (MMS), and one-sided selection (OSS) to balance the distribution of classes. The method also utilizes condensed nearest neighbors (CNN) to remove noisy majority examples and redundant examples. The proposed technique is twofold, involving the creation of artificial instances using SMOTE-OSS-CNN to oversample the under-represented class distribution and the use of MMS to train an instructor model that produces in-field knowledge for pseudo-labeled examples. The student model uses these pseudo-labels for supervised learning, and the student model and MMS meta-weigher are jointly trained to give each example subtask-specific weights to balance class labels and mitigate the noise effects caused by self-training. The proposed technique is evaluated on a discharge summary dataset against six state-of-the-art approaches, and the results demonstrate that it outperforms these approaches with complete labeled data and achieves results equivalent to state-of-the-art methods that require all labeled data using aspect-based sentiment analysis (ABSA). Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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15 pages, 743 KiB  
Article
MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification
by Hao Yu and Xinfu Li
Appl. Sci. 2024, 14(1), 164; https://doi.org/10.3390/app14010164 - 24 Dec 2023
Viewed by 988
Abstract
Artificially generated datasets often exhibit biases, leading conventional deep neural networks to overfit. Typically, a weighted function adjusts sample impact during model updates using weighted loss. Meta-neural networks, trained with meta-learning principles, generalize well across tasks, acquiring generalized weights. This enables the self-generation [...] Read more.
Artificially generated datasets often exhibit biases, leading conventional deep neural networks to overfit. Typically, a weighted function adjusts sample impact during model updates using weighted loss. Meta-neural networks, trained with meta-learning principles, generalize well across tasks, acquiring generalized weights. This enables the self-generation of tailored weighted functions for data biases. However, datasets may simultaneously exhibit imbalanced classes and corrupted labels, posing a challenge for current meta-models. To address this, this paper presents Meta-Loss Reweighting Network (MLRNet) with fusion attention features. MLRNet continually evolves sample loss values, integrating them with sample features from self-attention layers in a semantic space. This enhances discriminative power for biased samples. By employing minimal unbiased meta-data for guidance, mutual optimization between the classifier and the meta-model is conducted, endowing biased samples with more reasonable weights. Experiments on English and Chinese benchmark datasets including artificial and real-world biased data show MLRNet’s superior performance under biased data conditions. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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20 pages, 296 KiB  
Article
Non-Axiomatic Logic Modeling of English Texts for Knowledge Discovery and Commonsense Reasoning
by Osiris Juárez, Salvador Godoy-Calderon and Hiram Calvo
Appl. Sci. 2023, 13(20), 11535; https://doi.org/10.3390/app132011535 - 21 Oct 2023
Viewed by 1355
Abstract
Non-axiomatic logic (NAL) is a term-based, non-monotonic, multi-valued logic with evidence-based formal semantics. All those characteristics position NAL as an excellent candidate for modeling natural language expressions and supporting artificial agents while performing knowledge discovery and commonsense reasoning tasks. In this article, we [...] Read more.
Non-axiomatic logic (NAL) is a term-based, non-monotonic, multi-valued logic with evidence-based formal semantics. All those characteristics position NAL as an excellent candidate for modeling natural language expressions and supporting artificial agents while performing knowledge discovery and commonsense reasoning tasks. In this article, we propose a set of rules for the automatic translation of natural language (NL) text into the formal language of non-axiomatic logic (NAL). Several free available tools are used to support a previous linguistic analysis, and a common sense ontology is used to populate a background knowledge base that helps to delimit the scope and the semantics of logical formulas translated. Experimentation shows our set to be the most comprehensive NL-to-NAL translation rule set known so far. Furthermore, we included an extensive set of examples to show how our proposed set of rules can be used for translating a wide range of English statements with varying grammatical structures. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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17 pages, 2196 KiB  
Article
Mimicking Human Verification Behavior for News Media Credibility Evaluation
by Weijian Fan, Yongbin Wang and Hongbin Hu
Appl. Sci. 2023, 13(17), 9553; https://doi.org/10.3390/app13179553 - 23 Aug 2023
Cited by 3 | Viewed by 1471
Abstract
The widespread popularity of digital technology has enabled the rapid dissemination of news. However, it has also led to the emergence of “fake news” and the development of a media ecosystem with serious prejudices. If early warnings about the source of fake news [...] Read more.
The widespread popularity of digital technology has enabled the rapid dissemination of news. However, it has also led to the emergence of “fake news” and the development of a media ecosystem with serious prejudices. If early warnings about the source of fake news are received, this provides better outcomes in preventing its spread. Therefore, the issue of understanding and evaluating the credibility of media has received increasing attention. This work proposes a model of evaluating news media credibility called MiBeMC, which mimics the structure of human verification behavior in networks. Specifically, we first construct an intramodule information feature extractor to simulate the semantic analysis behavior of human information reading. Then, we design a similarity module to mimic the process of obtaining additional information. We also construct an aggregation module. This simulates human verification of correlated content. Finally, we apply regularized adversarial training strategy to train the MiBeMC model. The ablation study results demonstrate the effectiveness of MiBeMC. For the CLEF-task4 development and test dataset, the performance of the MiBeMC over state-of-the-art baseline methods is evaluated and found to be superior. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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16 pages, 1482 KiB  
Article
F-ALBERT: A Distilled Model from a Two-Time Distillation System for Reduced Computational Complexity in ALBERT Model
by Kyeong-Hwan Kim and Chang-Sung Jeong
Appl. Sci. 2023, 13(17), 9530; https://doi.org/10.3390/app13179530 - 23 Aug 2023
Viewed by 1151
Abstract
Recently, language models based on the Transformer architecture have been predominantly used in AI natural language processing. These models, which have been proven to perform better with more parameters, have led to a significant increase in model size and computational load. ALBERT solves [...] Read more.
Recently, language models based on the Transformer architecture have been predominantly used in AI natural language processing. These models, which have been proven to perform better with more parameters, have led to a significant increase in model size and computational load. ALBERT solves this problem by significantly reducing the number of parameters it retains by repeatedly reusing parameters. Although ALBERT significantly reduces the parameters it maintains, it requires a computational load similar to the original language model due to the reuse process. In this study, we develop a distillation system that decreases the number of times the ALBERT model reuses parameters and progressively reduces the parameters being reused. We propose a representation in this distillation system that can effectively distill the knowledge of the original model and develop a new architecture with reduced computation. Through this system, F-ALBERT, which had about half the computational load compared to the ALBERT model, restored about 98% of the performance of the original model on the GLUE benchmark. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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16 pages, 1350 KiB  
Article
VPN: Variation on Prompt Tuning for Named-Entity Recognition
by Niu Hu, Xuan Zhou, Bing Xu, Hanqing Liu, Xiangjin Xie and Hai-Tao Zheng
Appl. Sci. 2023, 13(14), 8359; https://doi.org/10.3390/app13148359 - 19 Jul 2023
Cited by 1 | Viewed by 1504
Abstract
Recently, prompt-based methods have achieved a promising performance in many natural language processing benchmarks. Despite success in sentence-level classification tasks, prompt-based methods work poorly in token-level tasks, such as named entity recognition (NER), due to the sophisticated design of entity-related templates. Note that [...] Read more.
Recently, prompt-based methods have achieved a promising performance in many natural language processing benchmarks. Despite success in sentence-level classification tasks, prompt-based methods work poorly in token-level tasks, such as named entity recognition (NER), due to the sophisticated design of entity-related templates. Note that the nature of prompt tuning makes full use of the parameters of the mask language model (MLM) head, while previous methods solely utilized the last hidden layer of language models (LMs) and the power of the MLM head is overlooked. In this work, we discovered the characteristics of semantic feature changes in samples after being processed using MLMs. Based on this characteristic, we designed a prompt-tuning variant for NER tasks. We let the pre-trained model predict the label words derived from the training dataset at each position and fed the generated logits (non-normalized probability) to the CRF layer. We evaluated our method on three popular datasets, and the experiments showed that our proposed method outperforms the state-of-the-art model in all three Chinese datasets. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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21 pages, 607 KiB  
Article
The Detection of Fake News in Arabic Tweets Using Deep Learning
by Shatha Alyoubi, Manal Kalkatawi and Felwa Abukhodair
Appl. Sci. 2023, 13(14), 8209; https://doi.org/10.3390/app13148209 - 14 Jul 2023
Cited by 16 | Viewed by 3836
Abstract
Fake news has been around for a long time, but the rise of social networking applications over recent years has rapidly increased the growth of fake news among individuals. The absence of adequate procedures to combat fake news has aggravated the problem. Consequently, [...] Read more.
Fake news has been around for a long time, but the rise of social networking applications over recent years has rapidly increased the growth of fake news among individuals. The absence of adequate procedures to combat fake news has aggravated the problem. Consequently, fake news negatively impacts various aspects of life (economical, social, and political). Many individuals rely on Twitter as a news source, especially in the Arab region. Mostly, individuals are reading and sharing regardless of the truth behind the news. Identifying fake news manually on these open platforms would be challenging as they allow anyone to build networks and publish the news in real time. Therefore, creating an automatic system for recognizing news credibility on social networks relying on artificial intelligence techniques, including machine learning and deep learning, has attracted the attention of researchers. Using deep learning methods has shown promising results in recognizing fake news written in English. Limited work has been conducted in the area of news credibility recognition for the Arabic language. This work proposes a deep learning-based model to detect fake news on Twitter. The proposed model utilizes the news content and social context of the user who participated in the news dissemination. In seeking an effective detection model for fake news, we performed extensive experiments using two deep learning algorithms with varying word embedding models. The experiments were evaluated using a self-created dataset. The experimental results revealed that the MARBERT with the convolutional neural network (CNN) model scores a superior performance in terms of accuracy and an F1-score of 0.956. This finding proves that the proposed model accurately detects fake news in Arabic Tweets relating to various topics. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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17 pages, 420 KiB  
Article
An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models
by Maimoonah Ahmed, Abdelkader Ouda, Mohamed Abusharkh, Sandeep Kohli and Khushwant Rai
Appl. Sci. 2023, 13(12), 7126; https://doi.org/10.3390/app13127126 - 14 Jun 2023
Cited by 2 | Viewed by 2264
Abstract
This paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses unique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities inherent in the Japanese language add an additional layer [...] Read more.
This paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses unique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities inherent in the Japanese language add an additional layer of intricacy to the development of effective translation models within this specific domain. Our approach encompasses a range of essential steps, including preprocessing, data preparation, expert feedback acquisition, and linguistic analysis. These steps collectively contribute to the enhancement of machine learning model performance. The experimental results, presented in this study, evaluate three prominent alternatives considered for the final step of the transformer model. Through our methodology, which incorporates a modified version of NLP-Model-III, we achieved outstanding performance for the given problem, attaining an impressive BLEU score of 46.8. Furthermore, significant improvements of up to three points on the BLEU score were observed through hyperparameter fine-tuning. This research also involved the development of a novel dataset consisting of meticulously collected patent document data. The findings of this study provide valuable insights and contribute to the advancement of Japanese patent translation methodologies. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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16 pages, 2989 KiB  
Article
T5-Based Model for Abstractive Summarization: A Semi-Supervised Learning Approach with Consistency Loss Functions
by Mingye Wang, Pan Xie, Yao Du and Xiaohui Hu
Appl. Sci. 2023, 13(12), 7111; https://doi.org/10.3390/app13127111 - 14 Jun 2023
Cited by 7 | Viewed by 4503
Abstract
Text summarization is a prominent task in natural language processing (NLP) that condenses lengthy texts into concise summaries. Despite the success of existing supervised models, they often rely on datasets of well-constructed text pairs, which can be insufficient for languages with limited annotated [...] Read more.
Text summarization is a prominent task in natural language processing (NLP) that condenses lengthy texts into concise summaries. Despite the success of existing supervised models, they often rely on datasets of well-constructed text pairs, which can be insufficient for languages with limited annotated data, such as Chinese. To address this issue, we propose a semi-supervised learning method for text summarization. Our method is inspired by the cycle-consistent adversarial network (CycleGAN) and considers text summarization as a style transfer task. The model is trained by using a similar procedure and loss function to those of CycleGAN and learns to transfer the style of a document to its summary and vice versa. Our method can be applied to multiple languages, but this paper focuses on its performance on Chinese documents. We trained a T5-based model and evaluated it on two datasets, CSL and LCSTS, and the results demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Applied Intelligence in Natural Language Processing)
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