Abstract

This paper introduces a series of experiments with an ALBERT over match-LSTM network on the top of pre-trained word vectors, for accurate classification of intelligent question answering and thus the guarantee of precise information service. To improve the performance of data classification, a short text classification method based on an ALBERT and match-LSTM model was proposed to overcome the limitations of the classification process, such as few vocabularies, sparse features, large amount of data, lots of noise and poor normalization. In the model, Jieba word segmentation tools and agricultural dictionary were selected to text segmentation, GloVe algorithm was then adopted to expand the text characteristic and weighted word vector according to the text of key vector, bi-directional gated recurrent unit was applied to catch the context feature information and multi-convolutional neural networks were finally established to gain local multidimensional characteristics of text. Batch normalization, Dropout, Global Average Pooling and Global Max Pooling were utilized to solve overfitting problem. The results showed that the model could classify questions accurately, with a precision of 96.8%. Compared with other classification models, such as multi-SVM model and CNN model, ALBERT+match-LSTM had obvious advantages in classification performance in intelligent Agri-tech information service.

Highlights

  • Published: 30 July 2021It has been almost 60 years since the first successful knowledge-based question answering system for baseball was developed in 1963, but the intelligent QA machine for Chinese agriculture datasets is still Blue Ocean.Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil-1.1

  • This work aims to improve the performance of question answering on the National Agricultural Technology and Education Cloud Platform

  • Our model is evaluated on data from NJTG, which is based on big data, cloud computation and mobile technology, with all kinds of agricultural technology educational resources—we call it the wing of Chinese agricultural technology

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Summary

Introduction

Published: 30 July 2021It has been almost 60 years since the first successful knowledge-based question answering system for baseball was developed in 1963, but the intelligent QA machine for Chinese agriculture datasets is still Blue Ocean.Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil-1.1. It has been almost 60 years since the first successful knowledge-based question answering system for baseball was developed in 1963, but the intelligent QA machine for Chinese agriculture datasets is still Blue Ocean. Our model is evaluated on data from NJTG, which is based on big data, cloud computation and mobile technology, with all kinds of agricultural technology educational resources—we call it the wing of Chinese agricultural technology. Agricultural administration departments at all levels, such as agricultural experts, agricultural technology-extension officers and farmers, who can access online education, online consultation, achievement promotion and marketing will fasten the development of Chinese intelligent agriculture.

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