Research

13 pages, 328 KiB

Open AccessArticle

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 395

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

Open AccessArticle

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 - 02 Mar 2024

Viewed by 413

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

Open AccessArticle

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

Viewed by 1120

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

Open AccessArticle

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 597

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

Open AccessArticle

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 588

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

Open AccessArticle

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 838

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

Open AccessArticle

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 1 | Viewed by 730

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

Open AccessArticle

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 718

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

Open AccessArticle

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 991

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

Open AccessArticle

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 4 | Viewed by 2560

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

Open AccessArticle

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 1 | Viewed by 1369

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

Open AccessArticle

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 1 | Viewed by 2704

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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