A BERT-Based Hybrid Short Text Classification Model Incorporating CNN and Attention-Based BiGRU

Short text classification is a research focus for natural language processing (NLP), which is widely used in news classification, sentiment analysis, mail filtering, and other fields. In recent years, deep learning techniques are applied to text classification and have made some progress. Different from ordinary text classification, short text has the problem of less vocabulary and feature sparsity, which raise higher request for text semantic feature representation. To address this issue, this paper proposes a feature fusion framework based on the bidirectional encoder representations from transformers (BERT). In this hybrid method, BERT is used to train word vector representation. Convolutional neural network (CNN) captures static features. As a supplement, a bi-gated recurrent neural network (BiGRU) is adopted to capture contextual features. Furthermore, an attention mechanism is introduced to assign the weight of salient words. The experimental results confirmed that the proposed model significantly outperforms the other state-of-the-art baseline methods.

Automated summary

The paper proposes a feature fusion framework based on BERT, where CNN captures static features, BiGRU captures contextual features, and an attention mechanism assigns weight to salient words. Experimental results show that the model outperforms other state-of-the-art baseline methods.