Low-Resource Fast Text Classification Based on Intra-Class and Inter-Class Distance Calculation

TL;DR

This paper introduces LFTC, a low-resource, fast text classification method that leverages intra-class and inter-class distance calculations to improve performance and efficiency, especially in resource-constrained settings.

Contribution

The paper proposes a novel low-resource, fast text classification model that uses class-specific compressors and distance-based similarity measures, reducing processing time and resource consumption.

Findings

Significant performance improvements on 9 benchmark datasets.

Reduced processing time compared to existing methods.

Effective in low-resource and limited data scenarios.

Abstract

In recent years, text classification methods based on neural networks and pre-trained models have gained increasing attention and demonstrated excellent performance. However, these methods still have some limitations in practical applications: (1) They typically focus only on the matching similarity between sentences. However, there exists implicit high-value information both within sentences of the same class and across different classes, which is very crucial for classification tasks. (2) Existing methods such as pre-trained language models and graph-based approaches often consume substantial memory for training and text-graph construction. (3) Although some low-resource methods can achieve good performance, they often suffer from excessively long processing times. To address these challenges, we propose a low-resource and fast text classification model called LFTC. Our approach begins by constructing a compressor list for each class to fully mine the regularity information within intra-class data. We then remove redundant information irrelevant to the target classification to reduce processing time. Finally, we compute the similarity distance between text pairs for classification. We evaluate LFTC on 9 publicly available benchmark datasets, and the results demonstrate significant improvements in performance and processing time, especially under limited computational and data resources, highlighting its superior advantages.