Social media has greatly accelerated the speed of information dissemination, but it has also inevitably led to a proliferation of rumors. Due to the deceptive nature of rumors, people often find it difficult to distinguish truth from falsehood, resul...
To address current issues in natural language spatiotemporal queries, including insufficient question semantic understanding, incomplete semantic information extraction, and inaccurate intent recognition, this paper proposes NL2Cypher, a DeBERTa (Dec...
Depression detection through social media analysis has emerged as a promising approach for early intervention and mental health support. This study evaluates the performance of various machine learning and transformer models in identifying depressive...
This study addresses a lexical ambiguity issue in Sesotho sa Leboa that arises from terms with various meanings, often known as homonyms or polysemous words. When compared to, for instance, European languages, this lexical ambiguity in Sesotho sa Leb...
Political misinformation spans fact-checked claims and news headlines, yet detection models are often evaluated within a single corpus and input representation. This study presents a metadata-flexible transformer framework evaluated on LIAR2 (19,212...
The manual classification of diplomatic documents by security sensitivity level is labor-intensive and inconsistent at scale. This study proposes SACHN-DeBERTa-v3-Large, a hybrid classification architecture integrating a DeBERTa-v3-Large backbone wit...
Regarding the existing models for feature extraction of complex similar entities, there are problems in the utilization of relative position information and the ability of key feature extraction. The distinctiveness of Chinese named entity recognitio...
Spam emails pose ongoing risks to digital security, including data breaches, privacy violations, and financial losses. Addressing the limitations of traditional detection systems in terms of accuracy, adaptability, and resilience remains a significan...
The extensive use of large language models (LLMs), particularly in the finance sector, raises concerns about the authenticity and reliability of generated text. Developing a robust method for distinguishing between human-written and AI-generated fina...
In semantic classification of katakana words using large language models and pre-trained language models, semantic divergences from original English meanings, such as those found in Wasei-Eigo which is Japanese-made English, and the inherent sense am...
The increasing sophistication of cyberattacks on smart grid infrastructure demands advanced anomaly detection and recovery systems that balance high recall rates with acceptable precision while providing reliable data restoration capabilities. This s...
Automated screening of political claims is challenging because many statements are short, underspecified, and labeled with fine-grained truthfulness categories that are difficult to separate using claim text alone. This study examines whether lightwe...
Few-shot stressor extraction from temporally evolving social-media text remains challenging, primarily because of unstable span boundaries and the imperfect correspondence between structural confidence and task-semantic validity. We propose VFR-MISE,...
Large Language Models (LLMs) have been rapidly evolving lately, resulting in the need for strong, explainable models to detect the difference between human-generated and machine-generated articles. Existing approaches which are mostly based on fine-t...
The complex and specialized terminology of financial language in Portuguese-speaking markets create significant challenges for natural language processing (NLP) applications, which must capture nuanced linguistic and contextual information to support...
In the contemporary field of geographic information, place name services serve as a core application support in geographic information science, widely applied in public services, cultural tourism, emergency management, and other scenarios. Place name...
The widespread rise of misinformation across digital platforms has increased the demand for accurate and efficient Fake News Detection (FND) systems. This study introduces an enhanced transformer-based architecture for FND, developed through comprehe...
The detection of corruption-related indicators within unstructured, textual procurement data remains a complex task due to linguistic ambiguity, contextual variation and domain-specific terminology. This study presents a comparative evaluation of thr...
This paper provides a pioneering examination and enhancement of generative chat models, with a specific focus on the BlenderBot 3 model. Through meticulous interaction with a diverse set of human participants, we dissected the fundamental components...
Maintaining code quality during software maintenance is a persistent challenge because bug-fix activities frequently introduce new code smells, accelerating long-term technical debt. Although bug-report classification has been applied to severity pre...
In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensit...
Detecting depression from user-generated content on social media platforms has garnered significant attention due to its potential for the early identification and monitoring of mental health issues. This paper presents a comprehensive approach for d...
Depression is now one of the most common mental health concerns in the digital era, calling for powerful computational tools for its detection and its level of severity estimation. A multi-level depression severity detection framework in the Reddit s...
Despite a few attempts to automatically crawl Ewe text from online news portals and magazines, the African Ewe language remains underdeveloped despite its rich morphology and complex "unique" structure. This is due to the poor quality, unbalanced, an...
This study compares the effectiveness of fine-tuning Transformer models, specifically BERT, RoBERTa, DeBERTa, and GPT-2, against using prompt engineering in LLMs like ChatGPT and GPT-4 for multi-class classification of hotel reviews. As the hospitali...
Due to the increasing harmfulness of software vulnerabilities, it is increasingly suggested to propose more efficient vulnerability assessment methods. However, existing methods mainly rely on manual updates and inefficient rule matching, and they st...
Natural language inference (NLI) is a fundamental and quite challenging task in natural language processing, requiring efficient methods that are able to determine whether given hypotheses derive from given premises. In this paper, we apply explainab...
Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly on social media platforms like X (formerly Twitter), to address the prevalent...
Developing an agricultural ontology will facilitate the advancement of agriculture in transferring information between fields and natural language processing (NLP). Grammatical and contextual comprehension of the domain data is required to construct...
The increasing demand for scalable and fair assessment of open-form responses in digital education shows the need for intelligent grading systems capable of balancing syntactic precision with semantic understanding. This study proposes a hybrid seman...
Sentiment analysis is widely used to quantify the emotional tone of text. However, many pretrained sentiment models are only designed for short inputs and can struggle when applied to longer compilations of text or even full documents. These limitati...
Rapid growth in IoT devices has led to the creation of crucial cybersecurity threats since existing methods of malware detection which use signature matching and numerical feature analysis are incapable of capturing the contextual behavioral semantic...
Knowledge graphs play a vital role in tasks such as recommendation systems, question-answering systems, and information retrieval. However, during practical construction, they commonly suffer from structural incompleteness and sparse relationships, w...
Binary code similarity detection (BCSD) plays a vital role in computer security and software engineering. Traditional BCSD methods heavily rely on specific features and necessitate rich expert knowledge, which are sensitive to code alterations. To im...
The escalating scale and psychological harm of cyberbullying across digital platforms present a critical social challenge, demanding the urgent development of highly accurate and reliable automated detection systems. Standard fine-tuned transformer m...
In this paper, we explore the ability of generative models to integrate deterministically hashed representations of information in the context of higher education. We compare BERT, RoBERTa, and GPT-2 base configurations for predicting undergraduate g...
This work explores how deep learning models, with different numbers of parameters, can be effectively applied to detect personal data within unstructured text using Named Entity Recognition (NER) techniques. We evaluate the performance of various arc...
Berta Cáceres’s life and struggle embody a biocultural ethic that conceives rivers as living co-inhabitants whose protection requires ethical commitment, collective organization, and institutional transformation. Her work offers a paradi...
The rising prevalence of social media turns them into huge, rich repositories of human emotions. Understanding and categorizing human emotion from social media content is of fundamental importance for many reasons, such as improvement of user experie...
Public opinion comments are important for the public to express their emotions and demands. Accordingly, identifying the public emotions contained in comments and taking corresponding countermeasures according to the changes in the emotions are of gr...
The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFS...
In the last decade, advances in statistical modeling and computer science have boosted the production of machine-produced contents in different fields: from language to image generation, the quality of the generated outputs is remarkably high, someti...
This work proposes a hybrid Uzbek sentiment analysis model (sometimes referred to as tonality analysis in the local literature) that integrates contextual text representations with named-entity information from an NER module and emoji-based emotional...
Multilingual sentiment analysis remains challenging because of cross-linguistic differences, uneven data availability, and potential shifts in meaning introduced during translation. Rather than proposing a new model, this study examines the performan...
Fault Localization (FL) has evolved with the emergence of the Transformer architecture. Traditionally, FL relied on generating a ranking of suspiciousness scores for each code unit using information obtained during program execution from a set of tes...
Full fine-tuning of pre-trained models sometimes requires inserting trainable layers into the middle of a pre-trained backbone, but such middle-layer insertion can severely degrade downstream performance. We hypothesize that this degradation arises b...
Computational analysis of therapeutic communication presents challenges in multi-label classification, severe class imbalance, and heterogeneous multimodal data integration. We introduce a bidirectional analytical framework addressing patient emotion...
In recent years, with the advancement of natural language processing techniques and the release of models like ChatGPT, how language models understand questions has become a hot topic. In handling complex logical reasoning with pre-trained models, it...
Stance detection has emerged as an essential tool in natural language processing for understanding how individuals express agreement, disagreement, or neutrality toward specific targets in social and online discourse. It plays a crucial role in bilin...
Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning (PEFT) technique for large language models (LLMs). LoRA’s benefits stem from its light weight and modular adapters. Standard LoRA applies adapters uniformly...
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