Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
UCSC NLP at SemEval-2026 Task 10: Boundary-Aware Span Extraction and RoBERTa Classification for Conspiracy Detection
We present our systems for SemEval-2026 Task 10 (PsyCoMark), addressing conspiracy marker extraction (Subtask 1) and document-level conspiracy detection (Subtask 2). For marker extraction, we formulate the task as multi-…
Document ClassificationRevising RVL-CDIP: Quantifying Errors and Test-Train Overlap
RVL-CDIP is a popular dataset for benchmarking document classifiers. However, the dataset contains ample amounts of label errors as well as non-trivial amounts of test-train overlap, both of which may impact model perfor…
Document ClassificationmoBERTo: A Modern Encoder for Portuguese via Continued Pretraining of ModernBERT
Encoder-only transformer models remain essential for production NLP pipelines. We introduce moBERTo, a Portuguese adaptation of ModernBERT obtained through continued pretraining of the ModernBERT-base checkpoint on 60 bi…
Natural Language UnderstandingDocument ClassificationInformation RetrievalEnhancing BiGRU with a KAN Block for Legal Document Classification and Summarization
This study introduces a novel architecture of KAN-based BiGRU model for the task of classification and summarization of legal documents in a low-resource multilingual setup. In order to tackle problems associated with do…
Document ClassificationSecurity Document Classification with a Fine-Tuned Local Large Language Model: Benchmark Data and an Open-Source System
Organizations that scan documents for sensitive information face a practical problem. Cloud services require data to be sent to external infrastructure, while rule-based tools often miss threats that depend on context. T…
Document ClassificationMulti-domain Multi-modal Document Classification Benchmark with a Multi-level Taxonomy
Document classification forms the backbone of modern enterprise content management, yet existing benchmarks remain trapped in oversimplified paradigms -- single domain settings with flat label structures -- that bear lit…
Document ClassificationG-Loss: Graph-Guided Fine-Tuning of Language Models
Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fa…
Document ClassificationSentiment AnalysisDuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs
Text-attributed graphs integrate semantic information of node texts with topological structure, offering significant value in various applications such as document classification and information extraction. Existing appr…
Document ClassificationInformation ExtractionDocument Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches
Information fusion is used widely to improve document classification by the integration of multiple data sources (multimodal) or representations (multiview). However, the field lacks a unified framework, a quantitative s…
Document ClassificationEvaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges
This work presents a comparative analysis of embedding-based and generative models for classifying geoscience technical documents. Using a multi-disciplinary benchmark dataset, we evaluated the trade-offs between model a…
Document ClassificationCommon TF-IDF variants arise as key components in the test statistic of a penalized likelihood-ratio test for word burstiness
TF-IDF is a classical formula that is widely used for identifying important terms within documents. We show that TF-IDF-like scores arise naturally from the test statistic of a penalized likelihood-ratio test setup captu…
Document ClassificationDocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion
Effective document intelligence models rely on large amounts of annotated training data. However, procuring sufficient and high-quality data poses significant challenges due to the labor-intensive and costly nature of da…
Key Information ExtractionDocument Layout AnalysisDocument ClassificationQuestion AnsweringExplainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models
Transformer models achieve state-of-the-art performance across domains and tasks, yet their deeply layered representations make their predictions difficult to interpret. Existing explainability methods rely on final-laye…
Document ClassificationHate Speech DetectionImage ClassificationSentiment AnalysisFrom Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-range dependencies and global document structure, especially for long texts.…
Document ClassificationDocument SummarizationDelete and Retain: Efficient Unlearning for Document Classification
Machine unlearning aims to efficiently remove the influence of specific training data from a model without full retraining. While much progress has been made in unlearning for LLMs, document classification models remain …
Document ClassificationSemimage: HSV-Based Semantic Image Encoding for Disentangled Text Representation
We propose SemImage, a novel method for representing a text document as a two-dimensional semantic image to be processed by convolutional neural networks (CNNs). In a SemImage, each word is represented as a pixel in a 2D…
Document ClassificationText ClassificationMulti-Task LearningGeneralized Sobolev IPM for Graph-Based Measures
We study the Sobolev IPM problem for measures supported on a graph metric space, where critic function is constrained to lie within the unit ball defined by Sobolev norm. While Le et al. (2025) achieved scalable computat…
Computational EfficiencyDocument ClassificationAutomated document processing system for government agencies using DBNET++ and BART models
An automatic document classification system is presented that detects textual content in images and classifies documents into four predefined categories (Invoice, Report, Letter, and Form). The system supports both offli…
Document ClassificationText ClassificationText DetectionComparison of Machine Learning Models to Classify Documents on Digital Development
Automated document classification is a trending topic in Natural Language Processing (NLP) due to the extensive growth in digital databases. However, a model that fits well for a specific classification task might perfor…
Document ClassificationBridging Computational Social Science and Deep Learning: Cultural Dissemination-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) have become vital in applications like document classification in citation networks, epidemic forecasting, viral marketing, user recommendation in social networks, and network monitoring. How…
Computational EfficiencyDocument ClassificationNode Classification