Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Efficient Classification of Long Documents Using Transformers
Several methods have been proposed for classifying long textual documents using Transformers. However, there is a lack of consensus on a benchmark to enable a fair comparison among different approaches. In this paper, we…
ClassificationDocument ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDocXClassifier: High Performance Explainable Deep Network for Document Image Classification
Convolutional Neural Networks (ConvNets) have been thoroughly researched for document image classification and are known for their exceptional performance in unimodal image-based document classification. Recently, howe…
ClassificationData AugmentationDocument Classificationdocument-image-classification+5A Survey of Historical Document Image Datasets
This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for histo…
Document ClassificationSurveySystematic Literature ReviewImproved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting
In document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real world events, e.g…
Document ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemi-supervised Nonnegative Matrix Factorization for Document Classification
We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators. The proposed SSNMF models simultaneousl…
ClassificationDocument ClassificationregressionSobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Optimal transport (OT) is a popular measure to compare probability distributions. However, OT suffers a few drawbacks such as (i) a high complexity for computation, (ii) indefiniteness which limits its applicability to k…
Document ClassificationTopological Data AnalysisWord EmbeddingsOne Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, diverse between each other, and that constit…
AllBayesian OptimizationDocument ClassificationHyperparameter Optimization+1Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired simi…
Citation PredictionContrastive LearningDocument ClassificationDocument Embedding+3Moving Other Way: Exploring Word Mover Distance Extensions
The word mover's distance (WMD) is a popular semantic similarity metric for two texts. This position paper studies several possible extensions of WMD. We experiment with the frequency of words in the corpus as a weightin…
ClassificationDocument ClassificationPositionSemantic Similarity+1Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely ClinicalBERT, also achieved state-of-the-ar…
Clinical KnowledgeDocument ClassificationFew-Shot Learningnamed-entity-recognition+5Importance of Textlines in Historical Document Classification
This paper describes a system prepared at Brno University of Technology for ICDAR 2021 Competition on Historical Document Classification, experiments leading to its design, and the main findings. The solved tasks include…
ClassificationDocument ClassificationWeakly Supervised ClassificationRecurrent Neural Networks with Mixed Hierarchical Structures and EM Algorithm for Natural Language Processing
How to obtain hierarchical representations with an increasing level of abstraction becomes one of the key issues of learning with deep neural networks. A variety of RNN models have recently been proposed to incorporate b…
Document ClassificationAutomation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability Study
In the process of Systematic Literature Review, citation screening is estimated to be one of the most time-consuming steps. Multiple approaches to automate it using various machine learning techniques have been proposed.…
Document ClassificationSystematic Literature ReviewWord EmbeddingsHierarchical Neural Network Approaches for Long Document Classification
Text classification algorithms investigate the intricate relationships between words or phrases and attempt to deduce the document's interpretation. In the last few years, these algorithms have progressed tremendously. T…
ClassificationDocument ClassificationSentencetext-classification+2Document Classification with Word Sense Knowledge
The performance of Word Sense Disambiguation (WSD) on a standard evaluation framework has reached an estimated upper bound. However, there is limited research on the application of WSD to relevant NLP tasks due to the hi…
ClassificationDocument ClassificationWord Sense DisambiguationERNIE-Layout: Layout-Knowledge Enhanced Multi-modal Pre-training for Document Understanding
We propose ERNIE-Layout, a knowledge enhanced pre-training approach for visual document understanding, which incorporates layout-knowledge into the pre-training of visual document understanding to learn a better joint mu…
cross-modal alignmentDocument Classificationdocument understandingQuestion AnsweringIntelligent Document Processing -- Methods and Tools in the real world
The originality of this publication is to look at the subject of IDP (Intelligent Document Processing) from the perspective of an end-user and industrialist and not that of a Computer Science researcher. This domain is o…
Document ClassificationOptical Character Recognition (OCR)Sublinear Time Approximation of Text Similarity Matrices
We study algorithms for approximating pairwise similarity matrices that arise in natural language processing. Generally, computing a similarity matrix for $n$ data points requires $\Omega(n^2)$ similarity computations. T…
Document ClassificationSentenceSentence Similaritytext similarityAn Empirical Study on Transfer Learning for Privilege Review
Protecting privileged communications and data from inadvertent disclosure is a paramount task in the US legal practice. Traditionally counsels rely on keyword searching and manual review to identify privileged documents …
BIG-bench Machine LearningDocument ClassificationTransfer LearningSparse Structure Learning via Graph Neural Networks for Inductive Document Classification
Recently, graph neural networks (GNNs) have been widely used for document classification. However, most existing methods are based on static word co-occurrence graphs without sentence-level information, which poses three…
ClassificationDocument ClassificationInductive LearningSentence