Papers Text Classification
“Text Classification” 태그가 달린 논문 3,864편 · 필터 해제
The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP
Digital healthcare generates vast amounts of clinical text that can support AI-assisted applications, yet German biomedical language models remain limited by older architectures or restricted training data. We present Ch…
Medical Named Entity RecognitionText ClassificationDomain AdaptationLingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection
Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all three subtasks: (1) binary polarization detection, (2) polarization…
Text ClassificationAutoTail-BSFGM: Class-Balance-Aware Fine-Tuning for Chinese Scholarly Text Classification
Scholarly text classification supports literature organization, subject indexing, and research intelligence, but Chinese scholarly corpora often contain imbalanced and semantically adjacent disciplinary labels. We propos…
Text ClassificationData filtering methods for training language models
Data quality is a critical factor in the effectiveness of machine learning models. Label errors, present even in widely used benchmarks, introduce noise into training data and reduce model generalization. In this work, w…
Linguistic AcceptabilityEmotion ClassificationLabel Error DetectionText ClassificationQuantifying and Optimizing Simplicity via Polynomial Representations
Deep networks often exhibit a preference for "simple" solutions, and such a simplicity bias is widely believed to play a key role in generalization. Yet a broadly applicable, quantitative measure of simplicity remains el…
Reinforcement LearningText ClassificationidSCD: Identifying Training Datasets through Semantic Correlation Descriptors
Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model's learned semantic correlation structure: incidental regularities tha…
Natural Language InferenceEmotion ClassificationText ClassificationStructured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited reasoning on complex text and lacks broader model transparency, while di…
Reinforcement LearningText ClassificationInterpretable Discriminative Text Representations via Agreement and Label Disentanglement
Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply. Existing discriminative representations often use anonymous embeddi…
Text ClassificationClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks
Text classification models are typically trained via supervised fine-tuning (SFT). However, SFT essentially performs behavior cloning from instance-wise labels and thus fails to adequately capture relative preference rel…
Reinforcement LearningText Classification1GC-7RC: One Graphic Card -- Seven Research Challenges! How Good Are AI Agents at Doing Your Job?
Autonomous AI coding agents are becoming a core tool for ML practitioners in industry and research alike. Despite this growing adoption, no standardized benchmark exists to evaluate their ability to design, implement, an…
Semantic SegmentationImage ClassificationText ClassificationGraph LearningAgentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search,…
Text ClassificationLLMs as annotators of credibility assessment in Danish asylum decisions: evaluating classification performance and errors beyond aggregated metrics
Off-the-shelf large language models (LLMs) are increasingly used to automate text annotation, yet their effectiveness remains underexplored for underrepresented languages and specialized domains where the class definitio…
Text Classificationcantnlp@DravidianLangTech 2026: organic domain adaptation improves multi-class hope speech detection in Tulu
This paper presents our systems and results for the Hope Speech Detection in Code-Mixed Tulu Language shared task at the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangT…
Hope Speech DetectionText ClassificationDomain AdaptationTraining with Harnesses: On-Policy Harness Self-Distillation for Complex Reasoning
Inference-time harnesses substantially improve large language models on complex reasoning tasks. However, the intrinsic capabilities of the underlying model remain unchanged by the addition of these external workflows. T…
Mathematical ReasoningText ClassificationWhen More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity
Federated learning (FL) is increasingly used to fine-tune foundation models (FMs) on distributed private data. The community largely assumes that large-scale pretraining serves as a 'rising tide that lifts all boats' in …
Text ClassificationFederated LearningCapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis
Wireless capsule endoscopy (WCE) enables non-invasive visual assessment of the small bowel, but its clinical utility is constrained by the large volume of frames generated per examination and the difficulty of recognisin…
Representation LearningCross-Modal RetrievalText ClassificationImage RetrievalBenchmarking PyCaret AutoML Against BiLSTM for Fine-Grained Emotion Classification: A Comparative Study on 20-Class Emotion Detection
Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task in natural language processing. This study benchmarks classical mach…
Emotion ClassificationText ClassificationModTGCN: Modularity-aware Graph Neural Networks for Text Classification
Graph-based text classification models typically rely on local neighborhood aggregation and overlook global community structure, despite semantic document graphs exhibiting strong class-consistent clustering. Ignoring th…
Graph Neural NetworkText ClassificationBug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics
Software quality assurance remains a major challenge in industrial environments, where large-scale and long-lived systems inevitably accumulate defects. Identifying the location of a fault is often time-consuming and cos…
Text ClassificationData AugmentationADE: Adaptive Dictionary Embeddings -- Scaling Multi-Anchor Representations to Large Language Models
Word embeddings are fundamental to natural language processing, yet traditional approaches represent each word with a single vector, creating representational bottlenecks for polysemous words and limiting semantic expres…
Text Classification