Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks (2020.acl-main)
Copied to clipboard
| Challenge: | Hierarchical text classification is a complex task that requires extended training time and a large number of parameters. |
| Approach: | They propose a top-up-classification task using dictionaries and auxiliary task from external dictionary definitions. |
| Outcome: | The proposed method outperforms previous studies using a reduced number of parameters in two well-known English datasets. |
Similar Papers
A Hierarchical Neural Attention-based Text Classifier (D18-1)
Copied to clipboard
| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
| Approach: | They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents. |
| Outcome: | The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. |
Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)
Copied to clipboard
| Challenge: | Text classification is a primary task in natural language processing (NLP). |
| Approach: | They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters. |
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. |
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. |
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. |
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. |
NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy. |
| Approach: | They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies . |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets. |
Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)
Copied to clipboard
| 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. |
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus (2021.emnlp-main)
Copied to clipboard
| Challenge: | Fig. 1 shows a simplified CT protocol. |
| Approach: | They propose to use geometric deep learning to classify hierarchical documents into different categories by using a selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. |
| Outcome: | The proposed model achieves f1-scores around 0.85 on a publicly available large scale CT registry of around 360K protocols. |