Challenge: Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data.
Approach: They propose a hierarchical text classification framework that can achieve both label controllability and text diversity by extracting high-quality hierarchic label information.
Outcome: The proposed method can achieve label controllability and text diversity by extracting high-quality hierarchical label information.

Similar Papers

HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (2024.eacl-long)

Copied to clipboard

Challenge: Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation.
Approach: They propose a text-generation-based framework that uses language models to encode dynamic text representations.
Outcome: The proposed framework surpasses existing methods while handling data and mitigating class imbalance.
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification (2024.lrec-main)

Copied to clipboard

Challenge: Hierarchical text classification (HTC) is a challenging subtask due to its complex taxonomic structure.
Approach: They propose a local hierarchy framework that can fit in nearly all HTC models and optimize them with the local hierarchy as auxiliary information.
Outcome: The proposed framework is effective in all scenarios and is adept at dealing with complex taxonomic hierarchies.
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification (2022.acl-long)

Copied to clipboard

Challenge: Existing methods encode text and label hierarchy separately and mix their representations for classification, where the hierarchy remains unchanged for all input text.
Approach: They propose to embed hierarchy into a text encoder by combining input and output data to generate a hierarchy-aware representation.
Outcome: Extensive experiments on three benchmark datasets verify the effectiveness of the proposed model.
Enhancing Hierarchical Text Classification through Knowledge Graph Integration (2023.findings-acl)

Copied to clipboard

Challenge: Existing approaches to hierarchical text classification are limited by lack of domain knowledge, which leads to mistakes in a variety of situations.
Approach: They propose a Knowledge-enabled Hierarchical Text Classification model which integrates knowledge graphs into HTC to address the knowledge limitations of traditional methods.
Outcome: The proposed model integrates knowledge graphs into the hierarchical text classification process, addressing the knowledge limitations of traditional methods.
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification (2021.acl-long)

Copied to clipboard

Challenge: Existing methods for hierarchical text classification focus on modeling the text, but the concept of sharing among classes has been ignored in previous work.
Approach: They propose a concept-based method that explicitly represents the concept and model the sharing mechanism among classes for the hierarchical text classification.
Outcome: The proposed method outperforms state-of-the-art methods on two widely used datasets.
Well Begun Is Half Done: An Implicitly Augmented Generative Framework with Distribution Modification for Hierarchical Text Classification (2024.lrec-main)

Copied to clipboard

Challenge: Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text.
Approach: They propose an explicit-agmented-generativ-e framework with distribution modification for hierarchical text classification.
Outcome: The proposed framework improves on the initial distributions of tail classes and avoids misinterpreting predictions on unbalanced data.
HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization (2021.naacl-main)

Copied to clipboard

Challenge: Existing models for hierarchical text classification do not consider statistical constraint on label representations learned by structure encoder.
Approach: They propose a new hierarchical text classification model called HTCInfoMax which incorporates two modules to improve the model's representations.
Outcome: The proposed model can model the interaction between each text sample and its ground truth labels explicitly which filters out irrelevant information.
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification (2023.findings-emnlp)

Copied to clipboard

Challenge: Existing approaches to hierarchical multi-label text classification (HMTC) ignore the correlation between similar samples and introduce noise .
Approach: They propose a semi-supervised method that uses a label hierarchy to bring text and label embeddings closer to each other by supervised contrastive learning.
Outcome: The proposed method bridges the gap between supervised contrastive learning and HMTC by bringing text and label embeddings closer.
Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations (2021.emnlp-main)

Copied to clipboard

Challenge: Existing approaches to hierarchical multi-label text classification ignore vertical category correlations or exploit dependencies across levels without considering horizontal correlations .
Approach: They propose a hierarchical multi-label text classification framework that considers both vertical and horizontal category correlations.
Outcome: The proposed framework improves on real-world HMTC datasets with significant improvements over baselines.
Hierarchical Label Generation for Text Classification (2023.findings-eacl)

Copied to clipboard

Challenge: None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document.
Approach: They propose a method that captures the label hierarchy for real-world classification applications by using a taxonomic hierarchy.
Outcome: The proposed method can generate unseen labels in subword level.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations