Papers Multi-Label Text Classification
“Multi-Label Text Classification” 태그가 달린 논문 181편 · 필터 해제
Exploring space efficiency in a tree-based linear model for extreme multi-label classification
Extreme multi-label classification (XMC) aims to identify relevant subsets from numerous labels. Among the various approaches for XMC, tree-based linear models are effective due to their superior efficiency and simplicit…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3A Debiased Nearest Neighbors Framework for Multi-Label Text Classification
Multi-Label Text Classification (MLTC) is a practical yet challenging task that involves assigning multiple non-exclusive labels to each document. Previous studies primarily focus on capturing label correlations to assis…
Contrastive LearningMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1A multi-level multi-label text classification dataset of 19th century Ottoman and Russian literary and critical texts
This paper introduces a multi-level, multi-label text classification dataset comprising over 3000 documents. The dataset features literary and critical texts from 19th-century Ottoman Turkish and Russian. It is the first…
ArticlesMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1Open-world Multi-label Text Classification with Extremely Weak Supervision
We study open-world multi-label text classification under extremely weak supervision (XWS), where the user only provides a brief description for classification objectives without any labels or ground-truth label space. S…
Keyword ExtractionLanguage ModellingLarge Language ModelMulti-Label Classification+5LegalTurk Optimized BERT for Multi-Label Text Classification and NER
The introduction of the Transformer neural network, along with techniques like self-supervised pre-training and transfer learning, has paved the way for advanced models like BERT. Despite BERT's impressive performance, o…
Multi Label Text ClassificationMulti-Label Text ClassificationNERtext-classification+2ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks
This study investigates the challenges posed by the dynamic nature of legal multi-label text classification tasks, where legal concepts evolve over time. Existing models often overlook the temporal dimension in their tra…
Continual LearningMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1Learning label-label correlations in Extreme Multi-label Classification via Label Features
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly fo…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+4Empowering Interdisciplinary Research with BERT-Based Models: An Approach Through SciBERT-CNN with Topic Modeling
Researchers must stay current in their fields by regularly reviewing academic literature, a task complicated by the daily publication of thousands of papers. Traditional multi-label text classification methods often igno…
ClassificationMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification
Learning an effective representation in multi-label text classification (MLTC) is a significant challenge in NLP. This challenge arises from the inherent complexity of the task, which is shaped by two key factors: the in…
Contrastive LearningMulti-class ClassificationMulti Label Text ClassificationMulti-Label Text Classification+2TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
We identify a critical bias in contemporary CLIP-based models, which we denote as single tag bias. This bias manifests as a disproportionate focus on a singular tag (word) while neglecting other pertinent tags, stemming …
Multi-Label Text ClassificationOpen Vocabulary Semantic SegmentationSemantic SegmentationTAG+1KeNet:Knowledge-enhanced Doc-Label Attention Network for Multi-label text classification
Multi-Label Text Classification (MLTC) is a fundamental task in the field of Natural Language Processing (NLP) that involves the assignment of multiple labels to a given text. MLTC has gained significant importance and h…
Information RetrievalMulti Label Text ClassificationMulti-Label Text ClassificationRecommendation Systems+3HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification
Hierarchical text classification (HTC) is a complex subtask under multi-label text classification, characterized by a hierarchical label taxonomy and data imbalance. The best-performing models aim to learn a static repre…
ArticlesLanguage ModelingLanguage ModellingMulti Label Text Classification+4Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
Within the scope of natural language processing, the domain of multi-label text classification is uniquely challenging due to its expansive and uneven label distribution. The complexity deepens due to the demand for an e…
Active LearningMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach
Despite significant advancements in multi-label text classification, the ability of existing models to generalize to novel and seldom-encountered complex concepts, which are compositions of elementary ones, remains under…
ClassificationData AugmentationMulti Label Text ClassificationMulti-Label Text Classification+3Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels
We extend our previous work on Inductive Conformal Prediction (ICP) for multi-label text classification and present a novel approach for addressing the computational inefficiency of the Label Powerset (LP) ICP, arrising …
Conformal PredictionMulti Label Text ClassificationMulti-Label Text ClassificationPrediction+3Multi-label Text Classification using GloVe and Neural Network Models
This study addresses the challenges of multi-label text classification. The difficulties arise from imbalanced data sets, varied text lengths, and numerous subjective feature labels. Existing solutions include traditiona…
Multi Label Text ClassificationMulti-Label Text Classificationtext-classificationText ClassificationText2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities
Multi-label text classification is a critical task in the industry. It helps to extract structured information from large amount of textual data. We propose Text to Topic (Text2Topic), which achieves high multi-label cla…
Decision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality
Multi-Label Text Classification (MLTC) aims to assign the most relevant labels to each given text. Existing methods demonstrate that label dependency can help to improve the model's performance. However, the introduction…
AttributeCausal InferencecounterfactualMulti Label Text Classification+3DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction
Multi-label text classification (MLTC) tasks in the medical domain often face the long-tail label distribution problem. Prior works have explored hierarchical label structures to find relevant information for few-shot cl…
ClassificationKnowledge GraphsMedical DiagnosisMulti-Label Classification+5Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification
Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification. Recent approaches to HMTC deal with the problem of imposing an over-constrained premise on the output…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3