Papers News Classification
“News Classification” 태그가 달린 논문 86편 · 필터 해제
DunbaaBERT: From Sacrifice to Semantics
Large language models have achieved strong performance across many NLP tasks, yet Urdu remains comparatively underexplored due to limited resources and fragmented evaluation settings. To address this gap, we introduce Du…
Linguistic AcceptabilityNews ClassificationSentiment AnalysisHybrid Feature Combinations with CNN for Bangla Fake News Classification
Nowadays, people in Bangladesh frequently rely on the internet and social media for daily news instead of traditional newspapers. However, the spread of false Bangla news through these platforms poses risks and challenge…
News ClassificationAddressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approach
The growing spread of misinformation in digital media highlights the need for reliable fake news detection systems, yet progress in under-resourced languages such as Bangla is limited by small and imbalanced datasets. Th…
Fake News DetectionNews ClassificationMultiPress: A Multi-Agent Framework for Interpretable Multimodal News Classification
With the growing prevalence of multimodal news content, effective news topic classification demands models capable of jointly understanding and reasoning over heterogeneous data such as text and images. Existing methods …
News ClassificationMzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages
Decoder-only language models can be adapted to diverse tasks through instruction finetuning, but the extent to which this generalizes at small scale for low-resource languages remains unclear. We focus on the languages o…
Natural Language UnderstandingData-to-Text GenerationNews ClassificationLeft, Right, or Center? Evaluating LLM Framing in News Classification and Generation
Large Language Model (LLM) based summarization and text generation are increasingly used for producing and rewriting text, raising concerns about political framing in journalism where subtle wording choices can shape int…
News ClassificationText GenerationFake News Classification in Urdu: A Domain Adaptation Approach for a Low-Resource Language
Misinformation on social media is a widely acknowledged issue, and researchers worldwide are actively engaged in its detection. However, low-resource languages such as Urdu have received limited attention in this domain.…
News ClassificationDomain AdaptationLabelFusion: Fusing Large Language Models with Transformer Encoders for Robust Financial News Classification
Financial news plays a central role in shaping investor sentiment and short-term dynamics in commodity markets. Many downstream financial applications, such as commodity price prediction or sentiment modeling, therefore …
Text ClassificationNews ClassificationEnhancing Burmese News Classification with Kolmogorov-Arnold Network Head Fine-tuning
In low-resource languages like Burmese, classification tasks often fine-tune only the final classification layer, keeping pre-trained encoder weights frozen. While Multi-Layer Perceptrons (MLPs) are commonly used, their …
News ClassificationAttention-Guided Feature Fusion (AGFF) Model for Integrating Statistical and Semantic Features in News Text Classification
News text classification is a crucial task in natural language processing, essential for organizing and filtering the massive volume of digital content. Traditional methods typically rely on statistical features like ter…
News ClassificationText ClassificationEvaluating Subword Tokenization Techniques for Bengali: A Benchmark Study with BengaliBPE
Tokenization is an important first step in Natural Language Processing (NLP) pipelines because it decides how models learn and represent linguistic information. However, current subword tokenizers like SentencePiece or H…
News ClassificationMaiBERT: A Pre-training Corpus and Language Model for Low-Resourced Maithili Language
Natural Language Understanding (NLU) for low-resource languages remains a major challenge in NLP due to the scarcity of high-quality data and language-specific models. Maithili, despite being spoken by millions, lacks ad…
Natural Language UnderstandingNews ClassificationSentiment AnalysisUtilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages
Large Language Models (LLMs) excel in English, but their performance degrades significantly on low-resource languages (LRLs) due to English-centric training. While methods like LangBridge align LLMs with multilingual enc…
News ClassificationTri-Learn Graph Fusion Network for Attributed Graph Clustering
In recent years, models based on Graph Convolutional Networks (GCN) have made significant strides in the field of graph data analysis. However, challenges such as over-smoothing and over-compression remain when handling …
ClusteringDeep ClusteringGraph ClusteringNews ClassificationKAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification, addressing three tasks: imbalanced binary hate speech detection, balanced multiclass news class…
Sentence ClassificationLanguage IdentificationHate Speech DetectionNews ClassificationClimate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change
Climate-Eval is a comprehensive benchmark designed to evaluate natural language processing models across a broad range of tasks related to climate change. Climate-Eval aggregates existing datasets along with a newly deve…
News ClassificationQuestion Answeringtext-classificationText ClassificationSynthetic News Generation for Fake News Classification
This study explores the generation and evaluation of synthetic fake news through fact based manipulations using large language models (LLMs). We introduce a novel methodology that extracts key facts from real articles, m…
ArticlesClassificationFact VerificationFake News Detection+3Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data
Machine learning-based classifiers have been used for text classification, such as sentiment analysis, news classification, and toxic comment classification. However, supervised machine learning models often require larg…
Active LearningClassificationNews ClassificationSentiment Analysis+3A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit
In this paper, we propose an optimized Transformer model that integrates Bayesian algorithms with a Bidirectional Gated Recurrent Unit (BiGRU), and apply it to fake news classification for the first time. First, we emplo…
Bayesian OptimizationFake News DetectionNews ClassificationA Self-Learning Multimodal Approach for Fake News Detection
The rapid growth of social media has resulted in an explosion of online news content, leading to a significant increase in the spread of misleading or false information. While machine learning techniques have been widely…
Contrastive LearningFake News DetectionMisinformationNews Classification+1