Papers Multi-Label Text Classification
“Multi-Label Text Classification” 태그가 달린 논문 181편 · 필터 해제
Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift
Real-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machine learning systems. However, the extent to which architectural choices…
Multi-Label Text ClassificationImage ClassificationMADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events
Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification (UQ) to support human oversight. Multi-label text classification (ML…
Multi-Label Text ClassificationAstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics
Scientific multi-label text classification suffers from extreme class imbalance, where specialized terminology exhibits severe power-law distributions that challenge standard classification approaches. Existing scientifi…
Multi-Label Text ClassificationMulti-Label ClassificationDomain AdaptationAn Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously?
Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus …
Multi-Label Text ClassificationMulti-Label ClassificationDivide, Cache, Conquer: Dichotomic Prompting for Efficient Multi-Label LLM-Based Classification
We introduce a method for efficient multi-label text classification with large language models (LLMs), built on reformulating classification tasks as sequences of dichotomic (yes/no) decisions. Instead of generating all …
Multi-Label Text ClassificationMulti-Label ClassificationProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification
The rapid growth of user-generated text across digital platforms has intensified the need for interpretable models capable of fine-grained text classification and explanation. Existing prototype-based models offer intuit…
Multi-Label Text ClassificationOne Size Does Not Fit All: Exploring Variable Thresholds for Distance-Based Multi-Label Text Classification
Distance-based unsupervised text classification is a method within text classification that leverages the semantic similarity between a label and a text to determine label relevance. This method provides numerous benefit…
Unsupervised Text ClassificationMulti-Label Text ClassificationMulti-Label ClassificationInformation RetrievalNASP-T: A Fuzzy Neuro-Symbolic Transformer for Logic-Constrained Aviation Safety Report Classification
Deep transformer models excel at multi-label text classification but often violate domain logic that experts consider essential, an issue of particular concern in safety-critical applications. We propose a hybrid neuro-s…
Multi-Label Text ClassificationData AugmentationGLiClass: Generalist Lightweight Model for Sequence Classification Tasks
Classification is one of the most widespread tasks in AI applications, serving often as the first step in filtering, sorting, and categorizing data. Since modern AI systems must handle large volumes of input data and ear…
Multi-Label Text ClassificationInstruction FollowingFew-Shot LearningCombining Language and Topic Models for Hierarchical Text Classification
Hierarchical text classification (HTC) is a natural language processing task which has the objective of categorising text documents into a set of classes from a predefined structured class hierarchy. Recent HTC approache…
Multi-Label Text ClassificationNatural Language UnderstandingTopic ModelsKDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification
The increasing volume of healthcare textual data requires computationally efficient, yet highly accurate classification approaches able to handle the nuanced and complex nature of medical terminology. This research prese…
ClassificationHyperparameter OptimizationKnowledge DistillationModel Compression+4QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare Text Multi-Label Classification
The escalating volume of collected healthcare textual data presents a unique challenge for automated Multi-Label Text Classification (MLTC), which is primarily due to the scarcity of annotated texts for training and thei…
Computational EfficiencyEnsemble LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on Text
Masked language modeling has become a widely adopted unsupervised technique to pre-train language models. However, the process of selecting tokens for masking is random, and the percentage of masked tokens is typically f…
Authorship AttributionLanguage ModelingLanguage ModellingMasked Language Modeling+4Retrieval-augmented Encoders for Extreme Multi-label Text Classification
Extreme multi-label classification (XMC) seeks to find relevant labels from an extremely large label collection for a given text input. To tackle such a vast label space, current state-of-the-art methods fall into two ca…
Extreme Multi-Label ClassificationMemorizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Hierarchical Text Classification (HTC) vs. eXtreme Multilabel Classification (XML): Two Sides of the Same Medal
Assigning a subset of labels from a fixed pool of labels to a given input text is a text classification problem with many real-world applications, such as in recommender systems. Two separate research streams address thi…
ClassificationMulti Label Text ClassificationMulti-Label Text ClassificationRecommendation Systems+2A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data
In real-world applications, as data availability increases, obtaining labeled data for machine learning (ML) projects remains challenging due to the high costs and intensive efforts required for data annotation. Many ML …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text ClassificationMulti-Label Text Classification+2Large Language Models for Patient Comments Multi-Label Classification
Patient experience and care quality are crucial for a hospital's sustainability and reputation. The analysis of patient feedback offers valuable insight into patient satisfaction and outcomes. However, the unstructured n…
De-identificationFew-Shot LearningIn-Context LearningMulti-Label Classification+7Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification
State-of-the-art Extreme Multi-Label Text Classification (XMTC) models rely heavily on multi-label attention layers to focus on key tokens in input text, but obtaining optimal attention weights is challenging and resourc…
DecoderLearning-To-RankMulti Label Text ClassificationMulti-Label Text Classification+3A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference
ICD(International Classification of Diseases) coding involves assigning ICD codes to patients visit based on their medical notes. Considering ICD coding as a multi-label text classification task, researchers have develop…
Causal InferencecounterfactualCounterfactual ReasoningMulti Label Text Classification+3Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learning
Supervised contrastive learning has achieved remarkable success by leveraging label information; however, determining positive samples in multi-label scenarios remains a critical challenge. In multi-label supervised cont…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3