Efficient Agricultural Question Classification With a BERT-Enhanced DPCNN Model

Abstract

The application of big data technology in agricultural production has led to explosive growth in agricultural data. The accurate classification of agricultural questions from vast amounts of question-and-answer data is currently a prominent topic in text classification research. However, due to the characteristics of agricultural questions, such as short text, high specialization, and uneven sample distribution, relying on a single model for feature extraction and classification has limitations. To address this issue and improve the performance of agricultural question classification, we propose the fusion text classification model BERT-DPCNN, which combines the Bidirectional Encoder Representations from Transformer (BERT) model with the Deep Pyramid Convolution Neural Network (DPCNN). Firstly, the BERT pre-training model captures word-level semantic information for each question and generates hidden vectors containing sentence-level features using 12 layers of transformers. Secondly, the output word vectors are input into DPCNN to further extract local features of the word-level text and capture long-distance textual dependencies. Finally, we verified the effectiveness of our fusion model using a self-constructed agricultural question dataset. Comparative experiments demonstrate that BERT-DPCNN achieves superior classification results with an accuracy rate of 99.07%. To assess its generalization performance, we conducted comparison experiments on the Tsinghua News dataset. Experimental results show significant improvement in BERT-DPCNN’s classification performance on agricultural question datasets compared to other models, meeting requirements for question classification in agricultural question-answering systems.

Keywords

• Agricultural short text
• question classification
• fusion model
• contextual feature
• local feature