The task of summarization can be categorized into two methods, extractive and abstractive. Extractive summarization selects the salient sentences from the original document to form a summary while abstractive summarization interprets the original doc...
The BERT model is regarded as the cornerstone of various pre-trained large language models that have achieved promising results in recent years. This article investigates how to optimize the BERT model in terms of fine-tuning speed and prediction acc...
Computer systems and applications generate large amounts of logs to measure and record information, which is vital to protect the systems from malicious attacks and useful for repairing faults, especially with the rapid development of distributed com...
With the advancement of educational informatization, vast amounts of Chinese text are generated across online platforms and digital textbooks. Effectively classifying such text is essential for intelligent education systems. This study conducts a sys...
Pre-trained language models perform well in various natural language processing tasks. However, their large number of parameters poses significant challenges for edge devices with limited resources, greatly limiting their application in practical dep...
Dimensional aspect-based sentiment analysis (dimABSA) aims to recognize aspect-level quadruples from reviews, offering a fine-grained sentiment description for user opinions. A quadruple consists of aspect, category, opinion, and sentiment intensity,...
This paper presents an approach to identifying political fake news in Spanish using Transformer architectures. Current methodologies often overlook political news due to the lack of quality datasets, especially in Spanish. To address this, we created...
In recent years, Natural Language Processing (NLP) has become increasingly important for extracting new insights from unstructured text data, and pre-trained language models now have the ability to perform state-of-the-art tasks like topic modeling,...
The identification of optimal drug candidates is very important in drug discovery. Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug–target interactions (DTIs). In recent year...
Bidirectional Encoder Representations from Transformers (BERT) has gained increasing attention from researchers and practitioners as it has proven to be an invaluable technique in natural languages processing. This is mainly due to its unique feature...
The increasing volume of medical information available in digital format presents a significant challenge for researchers seeking to extract relevant information. Manually analyzing voluminous data is a time-consuming process that constrains research...
With the wide application of artificial intelligence represented by deep learning in natural language-processing tasks, the automated scoring of translations has also advanced and improved. This study aims to determine if the BERT-assist system can r...
As a significant percentage of disasters and fatal accidents still occur in the construction sector, it is legally obligatory to conduct workplace risk assessments to avoid accidents and enhance safety. Identifying harmful and hazardous elements is c...
The existing means for classifying new energy industry policies are mainly based on the theory of policy instruments and manual encoding, which are highly subjective, less reproducible, and inefficient, especially when dealing with large-scale policy...
Recent advances with large-scale pre-trained language models (e.g., BERT) have brought significant potential to natural language processing. However, the large model size hinders their use in IoT and edge devices. Several studies have utilized task-s...
Stock price prediction has been a subject of significant interest in the financial mathematics field. Recently, interest in natural language processing models has increased, and among them, transformer models, such as BERT and FinBERT, are attracting...
Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose...
This research primarily explores the application of Natural Language Processing (NLP) technology in precision financial fraud detection, with a particular focus on the implementation and optimization of the FinChain-BERT model. Firstly, the FinChain-...
22 April 2022
Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (B...
Fine-tuning a BERT-Base model for specific tasks, such as sentiment analysis, has become resource-intensive and often requires high computational power and memory. This paper introduces SCALE, a novel resource-efficient fine-tuning method that target...
Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the qu...
Aspect-based sentiment analysis (ABSA) aims to identify the sentiment of an aspect in a given sentence and thus can provide people with comprehensive information. However, many conventional methods need help to discover the linguistic knowledge impli...
On 30 January 2020, the World Health Organization announced a new coronavirus, which later turned out to be very dangerous. Since that date, COVID-19 has spread to become a pandemic that has now affected practically all regions in the world. Since th...
Model robustness is an important index in medical cybersecurity, and hard-negative samples in electronic medical records can provide more gradient information, which can effectively improve the robustness of a model. However, hard negatives pose diff...
Assessing teaching behavior is essential for improving instructional quality, particularly in Physical Education, where classroom interactions are fast-paced and complex. Traditional evaluation methods such as questionnaires, expert observations, and...
With the advent of pre-trained language models, many natural language processing tasks in various languages have achieved great success. Although some research has been conducted on fine-tuning BERT-based models for syntactic parsing, and several Ara...
Customers heavily depend on reviews for product information. Fake reviews may influence the perception of product quality, making online reviews less effective. ChatGPT’s (GPT-3.5 and GPT-4) ability to generate human-like reviews and responses...
As night tourism has become a key driver of the nighttime economy in Chinese cities, urban lighting landscapes—as core attractions for night tourism—call for systematic investigation into the relationship between their development effecti...
14 October 2022
The COVID-19 pandemic has created unprecedented burdens on people’s health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of publ...
Software-defined networking (SDN) represents a transformative shift in network architecture by decoupling the control plane from the data plane, enabling centralized and flexible management of network resources. However, this architectural shift intr...
Understanding the diagnostic goal of medical reports is valuable information for understanding patient flows. This work focuses on extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports:...
The incorporation of artificial intelligence in educational contexts has significantly transformed the support provided to students facing learning difficulties, facilitating both the management of their educational process and their emotions. Additi...
Classification problems are common activities in many different domains and supervised learning algorithms have shown great promise in these areas. The classification of goods in international trade in Brazil represents a real challenge due to the co...
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 p...
Identifying and categorizing proper nouns in text, known as named entity recognition (NER), is crucial for various natural language processing tasks. However, developing effective NER techniques for low-resource languages like Urdu poses challenges d...
Critical thinking, as a crucial component of 21st-century core competencies, poses significant challenges for effective assessment in educational evaluation. This study proposes an automated assessment method for critical thinking in middle school Ch...
The compaction and structuring of system logs facilitate and expedite anomaly and cyberattack detection processes using machine-learning techniques, while simultaneously reducing alert fatigue caused by false positives. In this work, we implemented a...
In today’s rapidly evolving digital landscape, customer reviews play a crucial role in shaping the reputation and success of hotels. Accurately analyzing and classifying the sentiment of these reviews offers valuable insights into customer sati...
Offensive content is a complex and multifaceted form of harmful material that targets individuals or groups. In recent years, offensive language (OL) has become increasingly harmful, as it incites violence and intolerance. The automatic identificatio...
(1) Background: To better carry out air pollution control and to assist in accurate investigations of air pollution, in this study, we fully explore the spatial distribution characteristics of air pollution complaint results and provide guidance for...
This study aims to provide insights into the COVID-19-related communication on Twitter in the Republic of Croatia. For that purpose, we developed an NL-based framework that enables automatic analysis of a large dataset of tweets in the Croatian langu...
With the rapid growth of the digital economy, the application of artificial intelligence (AI) technology has injected new momentum into persistent green innovation. Using data on Chinese A-share listed companies from 2010 to 2023, this article aims t...
Given the widespread accessibility of content creation and sharing, false information proliferation is a growing concern. Researchers typically tackle fake news detection (FND) in specific topics using binary classification. Our study addresses a mor...
Pre-conceptual schemas are a straightforward way to represent knowledge using controlled language regardless of context. Despite the benefits of using pre-conceptual schemas by humans, they present challenges when interpreted by computers. We propose...
Japanese adverbs are difficult to classify, with little progress made since the 1930s. Now in the age of large language models, linguists need a framework for lexical grouping that incorporates quantitative, evidence-based relationships rather than p...
For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through techno...
Background: Large language models (LLMs) have seen a significant boost recently in the field of natural language processing (NLP) due to their capabilities in analyzing words. These autoregressive models prove robust in classification tasks where tex...
This paper presents a mathematical analysis of semantic convergence in transformer-based language models, drawing inspiration from the concept of fractal self-similarity. We introduce and prove a novel theorem characterizing the gradient of embedding...
Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applicat...
This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis an...
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