Papers Multi-Label Text Classification
“Multi-Label Text Classification” 태그가 달린 논문 181편 · 필터 해제
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMC) seeks to find relevant labels from an extreme large label collection for a given text input. Many real-world applications can be formulated as XMC problems, such as recommend…
Multi Label Text ClassificationMulti-Label Text ClassificationRecommendation Systemstext-classification+1Modeling label correlations implicitly through latent label encodings for multi-label text classification
Multi-label text classification (MLTC) aims to assign a set of labels to each given document. Unlike single-label text classification methods that often focus on document representation learning, MLTC faces a key challen…
Inductive BiasMulti Label Text ClassificationMulti-Label Text ClassificationRepresentation Learning+2CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification
Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with s…
Multi Label Text ClassificationMulti-Label Text Classificationtext-classificationText ClassificationBalancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Resampling and re-weighting are common appr…
Document ClassificationMulti-Label Text ClassificationText ClassificationA Dutch Dataset for Cross-lingual Multilabel Toxicity Detection
Multi-label toxicity detection is highly prominent, with many research groups, companies, and individuals engaging with it through shared tasks and dedicated venues. This paper describes a cross-lingual approach to annot…
Multi Label Text ClassificationMulti-Label Text Classificationtext-classificationText ClassificationLabel-Specific Dual Graph Neural Network for Multi-Label Text Classification
Multi-label text classification is one of the fundamental tasks in natural language processing. Previous studies have difficulties to distinguish similar labels well because they learn the same document representations f…
Dynamic ReconstructionGraph Neural NetworkMulti Label Text ClassificationMulti-Label Text Classification+2ECLARE: Extreme Classification with Label Graph Correlations
Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels …
ClassificationExtreme Multi-Label ClassificationGraph EmbeddingMulti-Label Classification+6Small-Text: Active Learning for Text Classification in Python
We introduce small-text, an easy-to-use active learning library, which offers pool-based active learning for single- and multi-label text classification in Python. It features numerous pre-implemented state-of-the-art qu…
Active LearningClassificationGPUMulti Label Text Classification+3Label prompt for multi-label text classification
One of the key problems in multi-label text classification is how to take advantage of the correlation among labels. However, it is very challenging to directly model the correlations among labels in a complex and unknow…
ClassificationLanguage ModelingLanguage ModellingMulti Label Text Classification+3On Evaluation and Improvement of Tail Label Performance for Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging each document with the most relevant subset of labels from an extremely large label set. The most challenging part for machine learning methods is the …
Multi Label Text ClassificationMulti-Label Text Classificationtext-classificationText ClassificationMedical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention
Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the International Classification of Diseases(ICD). ICD code i…
Medical Code PredictionMedical DiagnosisMulti-Label Text ClassificationDataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China
The digital media, identified as computational propaganda provides a pathway for propaganda to expand its reach without limit. State-backed propaganda aims to shape the audiences' cognition toward entities in favor of a …
Multi Label Text ClassificationMulti-Label Text ClassificationPropaganda detectiontext-classification+1Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning
In multi-label text classification (MLTC), each given document is associated with a set of correlated labels. To capture label correlations, previous classifier-chain and sequence-to-sequence models transform MLTC to a s…
DiversityMulti Label Text ClassificationMulti-Label Text ClassificationMulti-Task Learning+3TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names
Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of classes from a taxonomic class hierarchy. Most existing HMTC methods train classifiers using massive human-labeled documents, wh…
ClassificationMulti Label Text ClassificationMulti-Label Text ClassificationNatural Language Inference+3BdLAN:BERTdoc Label Attention Networks for Multi-label text classification
Multi-label text classification (MLTC) brings us new challenge in Natural Language Processing (NLP) which aims at assigning multiple labels for a given document. Many real-world tasks can be view as MLTC, such as tag rec…
ClassificationInformation RetrievalMulti Label Text ClassificationMulti-Label Text Classification+4Active learning for medical code assignment
Machine Learning (ML) is widely used to automatically extract meaningful information from Electronic Health Records (EHR) to support operational, clinical, and financial decision-making. However, ML models require a larg…
Active LearningDecision MakingGeneral ClassificationMulti Label Text Classification+3Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning
Exploiting label hierarchies has become a promising approach to tackling the zero-shot multi-label text classification (ZS-MTC) problem. Conventional methods aim to learn a matching model between text and labels, using a…
BenchmarkingMulti Label Text ClassificationMulti-Label Text ClassificationNatural Language Inference+2LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classification, MLTC has a wider range of applications in practice. In th…
ClassificationGeneral ClassificationMulti Label Text ClassificationMulti-Label Text Classification+2MATCH: Metadata-Aware Text Classification in A Large Hierarchy
Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set. Commonly, the metadata of the given documents and the hierarchy of the labels are availa…
ClassificationGeneral ClassificationMulti Label Text ClassificationMulti-Label Text Classification+2Towards Robustness to Label Noise in Text Classification via Noise Modeling
Large datasets in NLP suffer from noisy labels, due to erroneous automatic and human annotation procedures. We study the problem of text classification with label noise, and aim to capture this noise through an auxiliary…
ClassificationGeneral ClassificationLearning with noisy labelsMulti-Label Text Classification+2