Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Political pledges reflect candidates' policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify thi…
Document ClassificationBenchmarking GPT-5 for biomedical natural language processing
Biomedical literature and clinical narratives pose multifaceted challenges for natural language understanding, from precise entity extraction and document synthesis to multi-step diagnostic reasoning. This study extends …
Natural Language UnderstandingDocument ClassificationRelation ExtractionEfficient Zero-Shot Long Document Classification by Reducing Context Through Sentence Ranking
Transformer-based models like BERT excel at short text classification but struggle with long document classification (LDC) due to input length limitations and computational inefficiencies. In this work, we propose an eff…
Document ClassificationText ClassificationA Feasibility Experiment on the Application of Predictive Coding to Instant Messaging Corpora
Predictive coding, the term used in the legal industry for document classification using machine learning, presents additional challenges when the dataset comprises instant messages, due to their informal nature and smal…
Dimensionality ReductionDocument ClassificationPredictive Multimodal Modeling of Diagnoses and Treatments in EHR
While the ICD code assignment problem has been widely studied, most works have focused on post-discharge document classification. Models for early forecasting of this information could be used for identifying health risk…
Document ClassificationDocVCE: Diffusion-based Visual Counterfactual Explanations for Document Image Classification
As black-box AI-driven decision-making systems become increasingly widespread in modern document processing workflows, improving their transparency and reliability has become critical, especially in high-stakes applicati…
Document Image ClassificationDocument ClassificationUser Perception of Attention Visualizations: Effects on Interpretability Across Evidence-Based Medical Documents
The attention mechanism is a core component of the Transformer architecture. Beyond improving performance, attention has been proposed as a mechanism for explainability via attention weights, which are associated with in…
Document ClassificationRethinking Graph-Based Document Classification: Learning Data-Driven Structures Beyond Heuristic Approaches
In document classification, graph-based models effectively capture document structure, overcoming sequence length limitations and enhancing contextual understanding. However, most existing graph document representations …
Document ClassificationGraph GenerationEvaluating Hierarchical Clinical Document Classification Using Reasoning-Based LLMs
This study evaluates how well large language models (LLMs) can classify ICD-10 codes from hospital discharge summaries, a critical but error-prone task in healthcare. Using 1,500 summaries from the MIMIC-IV dataset and f…
Document ClassificationCan Reasoning LLMs Enhance Clinical Document Classification?
Clinical document classification is essential for converting unstructured medical texts into standardised ICD-10 diagnoses, yet it faces challenges due to complex medical language, privacy constraints, and limited annota…
ClassificationDocument ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONText Chunking for Document Classification for Urban System Management using Large Language Models
Urban systems are managed using complex textual documentation that need coding and analysis to set requirements and evaluate built environment performance. This paper contributes to the study of applying large-language m…
ChunkingDocument ClassificationManagementEvaluating Negative Sampling Approaches for Neural Topic Models
Negative sampling has emerged as an effective technique that enables deep learning models to learn better representations by introducing the paradigm of learn-to-compare. The goal of this approach is to add robustness to…
Document ClassificationTopic ModelsConverting Transformers into DGNNs Form
Recent advances in deep learning have established Transformer architectures as the predominant modeling paradigm. Central to the success of Transformers is the self-attention mechanism, which scores the similarity betwee…
Computational EfficiencyDocument ClassificationFormGraph Neural NetworkCross-Entropy Attacks to Language Models via Rare Event Simulation
Black-box textual adversarial attacks are challenging due to the lack of model information and the discrete, non-differentiable nature of text. Existing methods often lack versatility for attacking different models, suff…
Document ClassificationSaliency RankingSentenceOn Importance of Layer Pruning for Smaller BERT Models and Low Resource Languages
This study explores the effectiveness of layer pruning for developing more efficient BERT models tailored to specific downstream tasks in low-resource languages. Our primary objective is to evaluate whether pruned BERT m…
ClassificationDocument ClassificationData-Driven Self-Supervised Graph Representation Learning
Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on…
Data AugmentationDocument ClassificationGraph Property PredictionGraph Representation Learning+3Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models
Classifying scanned documents is a challenging problem that involves image, layout, and text analysis for document understanding. Nevertheless, for certain benchmark datasets, notably RVL-CDIP, the state of the art is cl…
Document Classificationdocument-image-classificationDocument Image Classificationdocument understanding+2Extreme Multi-label Completion for Semantic Document Labelling with Taxonomy-Aware Parallel Learning
In Extreme Multi Label Completion (XMLCo), the objective is to predict the missing labels of a collection of documents. Together with XML Classification, XMLCo is arguably one of the most challenging document classificat…
Document ClassificationMissing LabelsMulti-Task LearningLabel Errors in the Tobacco3482 Dataset
Tobacco3482 is a widely used document classification benchmark dataset. However, our manual inspection of the entire dataset uncovers widespread ontological issues, especially large amounts of annotation label problems i…
Document ClassificationvalidWordVIS: A Color Worth A Thousand Words
Document classification is considered a critical element in automated document processing systems. In recent years multi-modal approaches have become increasingly popular for document classification. Despite their improv…
Document Classification