Chinese Short Text Classification by ERNIE Based on LTC_Block

Abstract

Short text classification, an important direction of the basic research of natural language processing, has extensive applications. Its effect depends on feature extraction methods and feature representation methods. This paper proposed an LTC_Block-based short text classification model named ERNIE to classify Chinese short texts and extract semantics in the corpus to address the polysemy problem in the text. In this model, LTC_Block, a double-channel structural unit composed of BiLSTM and TextCNN, was used to extract the contextual sequences and overall features of semantics, and residual connection was used to integrate features and further classify short texts. Experiments on two different datasets showed that ERNIE achieved a better classification effect than mainstream models, proving its feasibility and effectiveness.