Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding
Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks. Though existing document pre-trained models have achieved excellent performance …
Document Classificationdocument understandingLanguage ModelingLanguage Modelling+1Supervised Dictionary Learning with Auxiliary Covariates
Supervised dictionary learning (SDL) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. The goal of SDL is…
Dictionary LearningDocument ClassificationPneumonia DetectionChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths
Sequential data naturally have different lengths in many domains, with some very long sequences. As an important modeling tool, neural attention should capture long-range interaction in such sequences. However, most exis…
ChunkingDocument ClassificationLong-range modelingPositionKnowledge-based Document Classification with Shannon Entropy
Document classification is the detection specific content of interest in text documents. In contrast to the data-driven machine learning classifiers, knowledge-based classifiers can be constructed based on domain specifi…
BIG-bench Machine LearningClassificationDocument ClassificationLDRNet: Enabling Real-time Document Localization on Mobile Devices
While Identity Document Verification (IDV) technology on mobile devices becomes ubiquitous in modern business operations, the risk of identity theft and fraud is increasing. The identity document holder is normally requi…
Document ClassificationEnriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification
We present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak ex…
ArticlesClassificationDocument ClassificationEpidemiology+2ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification
Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks. However, the increasing complexity of su…
Decision MakingDocument ClassificationFake News Detectiontext-classification+1UMUTextStats: A linguistic feature extraction tool for Spanish
Feature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of the art con…
Author ProfilingAuthorship VerificationDocument ClassificationFeature Engineering+3Approximate Conditional Coverage & Calibration via Neural Model Approximations
A typical desideratum for quantifying the uncertainty from a classification model as a prediction set is class-conditional singleton set calibration. That is, such sets should map to the output of well-calibrated selecti…
ClassificationDocument ClassificationFact VerificationGrammatical Error Detection+3FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by …
16k4kDocument ClassificationGPU+2VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification
Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream task. In this paper, we approach the document cla…
Document ClassificationDocument Image ClassificationBabyBear: Cheap inference triage for expensive language models
Transformer language models provide superior accuracy over previous models but they are computationally and environmentally expensive. Borrowing the concept of model cascading from computer vision, we introduce BabyBear,…
Document ClassificationNamed Entity RecognitionNamed Entity Recognition (NER)Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem
Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose …
Document ClassificationTraveling Salesman ProblemWord EmbeddingsTowards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification
Predicting the approval chance of a patent application is a challenging problem involving multiple facets. The most crucial facet is arguably the novelty — 35 U.S. Code § 102 rejects more recent applications that have ve…
ClassificationDocument ClassificationRevisiting Transformer-based Models for Long Document Classification
The recent literature in text classification is biased towards short text sequences (e.g., sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents are common and they cannot be efficien…
ClassificationDocument Classificationtext-classificationText ClassificationAnalysis of Sparse Subspace Clustering: Experiments and Random Projection
Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner. As a technique that is used in many domains, such as face clustering, plant …
ClusteringDocument ClassificationFace ClusteringImage Segmentation+1An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex
Word embedding is a modern distributed word representations approach widely used in many natural language processing tasks. Converting the vocabulary in a legal document into a word embedding model facilitates subjecting…
Document ClassificationMachine TranslationWord EmbeddingsLinkBERT: Pretraining Language Models with Document Links
Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that s…
Document ClassificationLanguage ModelingLanguage ModellingMasked Language Modeling+10Specialized Document Embeddings for Aspect-based Similarity of Research Papers
Document embeddings and similarity measures underpin content-based recommender systems, whereby a document is commonly represented as a single generic embedding. However, similarity computed on single vector representati…
Document ClassificationRecommendation SystemsSegmentationInterpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking
Research Replication Prediction (RRP) is the task of predicting whether a published research result can be replicated or not. Building an interpretable neural text classifier for RRP promotes the understanding of why a r…
Document ClassificationSentence