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Hierarchical text classification and its foundations: A review of current research
While collections of documents are often annotated with hierarchically structured concepts,
the benefits of these structures are rarely taken into account by classification techniques …
the benefits of these structures are rarely taken into account by classification techniques …
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 …
Prompt-based learning framework for zero-shot cross-lingual text classification
K Feng, L Huang, K Wang, W Wei, R Zhang - Engineering Applications of …, 2024 - Elsevier
Cross-lingual text classification is a challenging task that aims to train classifiers with data in
one language, known as the source language, and apply the acquired knowledge to data in …
one language, known as the source language, and apply the acquired knowledge to data in …
Meta in-context learning makes large language models better zero and few-shot relation extractors
Relation extraction (RE) is an important task that aims to identify the relationships between
entities in texts. While large language models (LLMs) have revealed remarkable in-context …
entities in texts. While large language models (LLMs) have revealed remarkable in-context …
Recall, retrieve and reason: towards better in-context relation extraction
Relation extraction (RE) aims to identify relations between entities mentioned in texts.
Although large language models (LLMs) have demonstrated impressive in-context learning …
Although large language models (LLMs) have demonstrated impressive in-context learning …
Prompt-based label-aware framework for few-shot multi-label text classification
Prompt-based learning has demonstrated remarkable success in few-shot text classification,
outperforming the traditional fine-tuning approach. This method transforms a text input into a …
outperforming the traditional fine-tuning approach. This method transforms a text input into a …
A Novel Negative Sample Generation Method for Contrastive Learning in Hierarchical Text Classification
J Zhou, L Zhang, Y He, R Fan, L Zhang… - Proceedings of the 31st …, 2025 - aclanthology.org
Hierarchical text classification (HTC) is an important task in natural language processing
(NLP). Existing methods typically utilize both text features and the hierarchical structure of …
(NLP). Existing methods typically utilize both text features and the hierarchical structure of …
Dual prompt tuning based contrastive learning for hierarchical text classification
S **ong, Y Zhao, J Zhang, L Mengxiang… - Findings of the …, 2024 - aclanthology.org
Hierarchical text classification aims at categorizing texts into a multi-tiered tree-structured
hierarchy of labels. Existing methods pay more attention to capture hierarchy-aware text …
hierarchy of labels. Existing methods pay more attention to capture hierarchy-aware text …
[HTML][HTML] SPIRIT: Structural Entropy Guided Prefix Tuning for Hierarchical Text Classification
H Zhu, J **a, R Liu, B Deng - Entropy, 2025 - mdpi.com
Hierarchical text classification (HTC) is a challenging task that requires classifiers to solve a
series of multi-label subtasks considering hierarchical dependencies among labels. Recent …
series of multi-label subtasks considering hierarchical dependencies among labels. Recent …
Revisiting Hierarchical Text Classification: Inference and Metrics
Hierarchical text classification (HTC) is the task of assigning labels to a text within a
structured space organized as a hierarchy. Recent works treat HTC as a conventional …
structured space organized as a hierarchy. Recent works treat HTC as a conventional …