Incorporating hierarchy into text encoder: a contrastive learning approach for hierarchical text classification
Z Wang, P Wang, L Huang, X Sun, H Wang - ar** from image time series: Deep learning with multi-scale label hierarchies
The aim of this paper is to map agricultural crops by classifying satellite image time series.
Domain experts in agriculture work with crop type labels that are organised in a hierarchical …
Domain experts in agriculture work with crop type labels that are organised in a hierarchical …
Hierarchy-aware label semantics matching network for hierarchical text classification
Hierarchical text classification is an important yet challenging task due to the complex
structure of the label hierarchy. Existing methods ignore the semantic relationship between …
structure of the label hierarchy. Existing methods ignore the semantic relationship between …
Multi-label text classification using attention-based graph neural network
In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It
is observed that most MLTC tasks, there are dependencies or correlations among labels …
is observed that most MLTC tasks, there are dependencies or correlations among labels …
Retrieval-style in-context learning for few-shot hierarchical text classification
Hierarchical text classification (HTC) is an important task with broad applications, and few-
shot HTC has gained increasing interest recently. While in-context learning (ICL) with large …
shot HTC has gained increasing interest recently. While in-context learning (ICL) with large …
Hierarchy-aware global model for hierarchical text classification
Hierarchical text classification is an essential yet challenging subtask of multi-label text
classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the …
classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the …
HPT: Hierarchy-aware prompt tuning for hierarchical text classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification
due to its complex label hierarchy. Recently, the pretrained language models (PLM) have …
due to its complex label hierarchy. Recently, the pretrained language models (PLM) have …
Label relation graphs enhanced hierarchical residual network for hierarchical multi-granularity classification
Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity
labels to each object and focuses on encoding the label hierarchy, eg,[" Albatross"," Laysan …
labels to each object and focuses on encoding the label hierarchy, eg,[" Albatross"," Laysan …
Cognitive structure learning model for hierarchical multi-label text classification
B Wang, X Hu, P Li, SY Philip - Knowledge-Based Systems, 2021 - Elsevier
The human mind grows in learning new knowledge, which finally organizes and develops a
basic mental pattern called cognitive structure. Hierarchical multi-label text classification …
basic mental pattern called cognitive structure. Hierarchical multi-label text classification …
HTCInfoMax: A global model for hierarchical text classification via information maximization
The current state-of-the-art model HiAGM for hierarchical text classification has two
limitations. First, it correlates each text sample with all labels in the dataset which contains …
limitations. First, it correlates each text sample with all labels in the dataset which contains …