Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?
We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rap…
ClassificationDocument ClassificationHierarchical Multi-label ClassificationMulti-Label Classification+1HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effectiveness of RAG depends on the relevance …
Contrastive LearningDocument ClassificationRAGRepresentation Learning+2Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks
Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on NLP tasks, but their black-box nature, which leads to a lack of interpretability, has been a major…
ClassificationContrastive LearningDocument ClassificationGraph Attention+6Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts
The development of robust Document AI models has been constrained by limited access to high-quality, labeled datasets, primarily due to data privacy concerns, scarcity, and the high cost of manual annotation. Traditional…
Document AIDocument ClassificationDomain AdaptationImage Augmentation+6Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs
This paper proposes a Clustering, Labeling, then Augmenting framework that significantly enhances performance in Semi-Supervised Text Classification (SSTC) tasks, effectively addressing the challenge of vast datasets wit…
ClassificationClusteringData AugmentationDocument Classification+10Medical-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
Accurate classification of cancer-related medical abstracts is crucial for healthcare management and research. However, obtaining large, labeled datasets in the medical domain is challenging due to privacy concerns and t…
Document ClassificationGraph AttentionWeakly-supervised diagnosis identification from Italian discharge letters
Objective: Recognizing diseases from discharge letters is crucial for cohort selection and epidemiological analyses, as this is the only type of data consistently produced across hospitals. This is a classic document cla…
Document Classificationtext-classificationText ClassificationChuLo: Chunk-Level Key Information Representation for Long Document Processing
Transformer-based models have achieved remarkable success in various Natural Language Processing (NLP) tasks, yet their ability to handle long documents is constrained by computational limitations. Traditional approaches…
ChunkingClassificationDocument Classificationdocument understanding+5Text Classification using Graph Convolutional Networks: A Comprehensive Survey
Text classification is a quintessential and practical problem in natural language processing with applications in diverse domains such as sentiment analysis, fake news detection, medical diagnosis, and document classific…
ClassificationDocument ClassificationFake News DetectionMedical Diagnosis+4Orthogonal Nonnegative Matrix Factorization with the Kullback-Leibler divergence
Orthogonal nonnegative matrix factorization (ONMF) has become a standard approach for clustering. As far as we know, most works on ONMF rely on the Frobenius norm to assess the quality of the approximation. This paper pr…
Document ClassificationManual Verbalizer Enrichment for Few-Shot Text Classification
With the continuous development of pre-trained language models, prompt-based training becomes a well-adopted paradigm that drastically improves the exploitation of models for many natural language processing tasks. Promp…
BenchmarkingClassificationDocument ClassificationFew-Shot Learning+3Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes
Scientific document classification is a critical task and often involves many classes. However, collecting human-labeled data for many classes is expensive and usually leads to label-scarce scenarios. Moreover, recent wo…
ArticlesClassificationDocument ClassificationFew-Shot Learning+5Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification
Long document classification presents challenges in capturing both local and global dependencies due to their extensive content and complex structure. Existing methods often struggle with token limits and fail to adequat…
Document ClassificationGraph AttentionSentenceDocument Type Classification using File Names
Rapid document classification is critical in several time-sensitive applications like digital forensics and large-scale media classification. Traditional approaches that rely on heavy-duty deep learning models fall short…
ClassificationDocument ClassificationFLAG: Financial Long Document Classification via AMR-based GNN
The advent of large language models (LLMs) has initiated much research into their various financial applications. However, in applying LLMs on long documents, semantic relations are not explicitly incorporated, and a ful…
Abstract Meaning RepresentationDocument ClassificationStock PredictionStock Trend Prediction+3On Importance of Pruning and Distillation for Efficient Low Resource NLP
The rise of large transformer models has revolutionized Natural Language Processing, leading to significant advances in tasks like text classification. However, this progress demands substantial computational resources, …
Document ClassificationGPUKnowledge Distillationtext-classification+1SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization
NLP datasets may still contain annotation errors, even when they are manually annotated. Researchers have attempted to develop methods to automatically reduce the adverse effect of errors in datasets. However, existing m…
Document Classificationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4Out-of-Distribution Detection with Attention Head Masking for Multimodal Document Classification
Detecting out-of-distribution (OOD) data is crucial in machine learning applications to mitigate the risk of model overconfidence, thereby enhancing the reliability and safety of deployed systems. The majority of existin…
Document AIDocument ClassificationOut-of-Distribution DetectionAutoML-guided Fusion of Entity and LLM-based Representations for Document Classification
Large semantic knowledge bases are grounded in factual knowledge. However, recent approaches to dense text representations (i.e. embeddings) do not efficiently exploit these resources. Dense and robust representations of…
AutoMLClassificationDocument ClassificationLanguage Modeling+5Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification
Clinical machine learning research and AI driven clinical decision support models rely on clinically accurate labels. Manually extracting these labels with the help of clinical specialists is often time-consuming and exp…
ClassificationDocument Classification