Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
A Comparative Study of Pretrained Language Models for Long Clinical Text
Objective: Clinical knowledge enriched transformer models (e.g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks. One of the core limitations of these transformer models is…
Clinical KnowledgeDocument Classificationnamed-entity-recognitionNamed Entity Recognition+3FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs
We present FewShotTextGCN, a novel method designed to effectively utilize the properties of word-document graphs for improved learning in low-resource settings. We introduce K-hop Neighbourhood Regularization, a regulari…
Document ClassificationFew-Shot Learninggraph constructionLanguage Modeling+1ClassBases at CASE-2022 Multilingual Protest Event Detection Tasks: Multilingual Protest News Detection and Automatically Replicating Manually Created Event Datasets
In this report, we describe our ClassBases submissions to a shared task on multilingual protest event detection. For the multilingual protest news detection, we participated in subtask-1, subtask-2, and subtask-4, which …
ClassificationDocument ClassificationEvent DetectionSentence+3Multimodal Side-Tuning for Document Classification
In this paper, we propose to exploit the side-tuning framework for multimodal document classification. Side-tuning is a methodology for network adaptation recently introduced to solve some of the problems related to prev…
ClassificationDocument ClassificationDocument Image ClassificationTransfer LearningHawk: An Industrial-strength Multi-label Document Classifier
There are a plethora of methods and algorithms that solve the classical multi-label document classification. However, when it comes to deployment and usage in an industry setting, most, if not all the contemporary approa…
BenchmarkingDocument ClassificationSentenceTsetlin Machine Embedding: Representing Words Using Logical Expressions
Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating …
Document ClassificationMachine TranslationSentiment AnalysisHuman in the loop: How to effectively create coherent topics by manually labeling only a few documents per class
Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classif…
Document ClassificationFew-Shot LearningGeneralised Spherical Text Embedding
This paper aims to provide an unsupervised modelling approach that allows for a more flexible representation of text embeddings. It jointly encodes the words and the paragraphs as individual matrices of arbitrary column …
ClusteringDocument ClassificationSemantic Textual SimilarityText Representation Enrichment Utilizing Graph based Approaches: Stock Market Technical Analysis Case Study
Graph neural networks (GNNs) have been utilized for various natural language processing (NLP) tasks lately. The ability to encode corpus-wide features in graph representation made GNN models popular in various tasks such…
ClassificationDocument ClassificationLink PredictionNode Classification+1Extended Multilingual Protest News Detection -- Shared Task 1, CASE 2021 and 2022
We report results of the CASE 2022 Shared Task 1 on Multilingual Protest Event Detection. This task is a continuation of CASE 2021 that consists of four subtasks that are i) document classification, ii) sentence classifi…
Document ClassificationEvent DetectionEvent ExtractionSentence+1Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer
Pre-trained Transformers currently dominate most NLP tasks. They impose, however, limits on the maximum input length (512 sub-words in BERT), which are too restrictive in the legal domain. Even sparse-attention models, s…
Document ClassificationBioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models…
Document ClassificationLanguage ModellingQuestion AnsweringRelation Extraction+1Evaluating Out-of-Distribution Performance on Document Image Classifiers
The ability of a document classifier to handle inputs that are drawn from a distribution different from the training distribution is crucial for robust deployment and generalizability. The RVL-CDIP corpus is the de facto…
Document ClassificationLbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics
In this paper, we consider the task of retrieving documents with predefined topics from an unlabeled document dataset using an unsupervised approach. The proposed unsupervised approach requires only a small number of key…
Document ClassificationRetrievalUnsupervised Text ClassificationWorld KnowledgeContrastive Training Improves Zero-Shot Classification of Semi-structured Documents
We investigate semi-structured document classification in a zero-shot setting. Classification of semi-structured documents is more challenging than that of standard unstructured documents, as positional, layout, and styl…
ClassificationDocument Classificationzero-shot-classificationZero-Shot LearningAn Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification
Non-hierarchical sparse attention Transformer-based models, such as Longformer and Big Bird, are popular approaches to working with long documents. There are clear benefits to these approaches compared to the original Tr…
Document ClassificationGPUCNN-Trans-Enc: A CNN-Enhanced Transformer-Encoder On Top Of Static BERT representations for Document Classification
BERT achieves remarkable results in text classification tasks, it is yet not fully exploited, since only the last layer is used as a representation output for downstream classifiers. The most recent studies on the nature…
Document Classificationtext-classificationText ClassificationFlexible Job Classification with Zero-Shot Learning
Using a taxonomy to organize information requires classifying objects (documents, images, etc) with appropriate taxonomic classes. The flexible nature of zero-shot learning is appealing for this task because it allows cl…
ClassificationDocument ClassificationJob classificationRecommendation Systems+2BL.Research at SemEval-2022 Task 8: Using various Semantic Information to evaluate document-level Semantic Textual Similarity
This paper presents our system for document-level semantic textual similarity (STS) evaluation at SemEval-2022 Task 8: “Multilingual News Article Similarity”. The semantic information used is obtained by using different …
Document ClassificationSemantic Textual SimilaritySTSD2GCLF: Document-to-Graph Classifier for Legal Document Classification
Legal document classification is an essential task in law intelligence to automate the labor-intensive law case filing process. Unlike traditional document classification problems, legal documents should be classified by…
ClassificationDocument ClassificationGraph AttentionRelation