Assemble the shallow or integrate a deep? Toward a lightweight solution for glyph-aware Chinese text classification.

ABSTRACT: As hieroglyphic languages, such as Chinese, differ from alphabetic languages, researchers have always been interested in using internal glyph features to enhance semantic representation. However, the models used in such studies are becoming increasingly computationally expensive, even for simple tasks like text classification. In this paper, we aim to balance model performance and computation cost in glyph-aware Chinese text classification tasks. To address this issue, we propose a lightweight ensemble learning method for glyph-aware Chinese text classification (LEGACT) that consists of typical shallow networks as base learners and machine learning classifiers as meta-learners. Through model design and a series of experiments, we demonstrate that an ensemble approach integrating shallow ne

SUBMITTER: Hou J 

PROVIDER: S-EPMC10381045 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature