TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision

Conceitos Básicos

TELEClass proposes a method for minimally supervised hierarchical text classification, enhancing label taxonomy with class-indicative terms and leveraging LLMs tailored for the hierarchical label structure.

Resumo

Hierarchical text classification is essential in text mining. TELEClass enriches label taxonomy with topical terms, outperforming weakly-supervised methods and LLM-based zero-shot prompting on public datasets.

Hierarchical text classification categorizes documents into multiple classes. TELEClass enhances label taxonomy with class-indicative terms mined from the corpus. It utilizes LLMs for data annotation and creation tailored for the hierarchical label space. The method outperforms previous approaches on two public datasets.

Most earlier works focus on fully or semi-supervised methods requiring human annotated data. TELEClass minimizes supervision by using only class names of each node. Large language models show competitive performance but struggle in hierarchical settings due to structural information loss.

The proposed TELEClass method combines taxonomy enrichment and LLM enhancement for weakly-supervised hierarchical text classification. Experiments demonstrate its superiority over existing methods on public datasets.

Estatísticas

Recently, large language models (LLM) such as GPT-4 have demonstrated strong performance in flat text classification through zero-shot or few-shot prompting.
TaxoClass-NoST study the hierarchical text classification with minimal supervision, taking the class name as the only supervision signal.
Extensive experiments on two datasets show that TELEClass can outperform strong weakly-supervised hierarchical text classification methods and zero-shot LLM prompting methods.

Citações

Principais Insights Extraídos De

TELEClass

by Yunyi Zhang,... às arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00165.pdf

Perguntas Mais Profundas

How does TELEClass address challenges faced by large language models in hierarchical settings

TELEClass addresses challenges faced by large language models in hierarchical settings by integrating corpus-based taxonomy enrichment and leveraging LLMs tailored for the hierarchical label structure. Large language models, such as GPT-3.5-turbo, have shown strong performance in flat text classification tasks through zero-shot prompting. However, applying these models in a hierarchical setting with a large and structured label space remains challenging. Directly including hundreds of classes in prompts can lead to structural information loss, increased computational costs, and diminished clarity for LLMs to focus on critical information.
To tackle these challenges, TELEClass utilizes an LLM-enhanced core class annotation step that identifies document "core classes" using a textual entailment model for top-down candidate search on the taxonomy. It then enriches the label taxonomy with class-indicative topical terms mined from the text corpus to provide additional features for classification. By combining enriched taxonomies with refined core classes based on embedding-based similarity scores, TELEClass improves pseudo-label quality and classifier training efficiency in hierarchical settings.

What are the implications of minimizing human efforts in weakly-supervised hierarchical text classification

Minimizing human efforts in weakly-supervised hierarchical text classification has several implications:

Cost-effectiveness: Weakly-supervised methods like TELEClass reduce the need for extensive human annotation efforts required in fully supervised or semi-supervised approaches. This leads to cost savings as acquiring human-labeled data is often costly and time-consuming.

Scalability: By requiring only the class names of the label taxonomy as supervision, weakly-supervised methods like TELEClass are more scalable than fully supervised methods that rely on manually labeled data sets.

Efficiency: Minimally supervised approaches enable faster model development and deployment since they do not depend heavily on human annotators' availability or expertise.

Accessibility: Weaker supervision requirements make it easier for researchers and practitioners without specialized domain knowledge or resources to participate in text classification tasks effectively.

Overall, minimizing human efforts through weakly-supervised approaches like TELEClass makes hierarchical text classification more accessible, efficient, cost-effective, and scalable.

How can enriched label taxonomies improve document classification accuracy beyond traditional methods

Enriched label taxonomies can improve document classification accuracy beyond traditional methods by providing additional context-specific features that enhance the understanding of each class's unique characteristics within a hierarchy:

Improved Class Discrimination: Enriched taxonomies include class-indicative topical terms mined from the corpus that help distinguish between closely related classes within a hierarchy better than relying solely on class names.

Enhanced Semantic Understanding: The inclusion of enriched terms provides semantic richness to each class description beyond surface-level labels, enabling classifiers to capture nuanced distinctions between classes accurately.

Better Generalization: Enriched taxonomies offer more comprehensive representations of each class's content domain based on real-world usage patterns found in documents rather than relying solely on predefined labels.

4 .Increased Classification Precision: By incorporating additional features derived from enriched taxonomies during classifier training (as done by TELEClass), classifiers can learn more robust decision boundaries leading to improved accuracy when categorizing documents into multiple nodes within a complex hierarchy.
These implications highlight how enriched label taxonomies play a crucial role in enhancing document classification accuracy by providing richer contextual information essential for effective machine learning algorithms' performance across diverse datasets and domains beyond what traditional methods alone can achieve