您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese

文献类型： 外文期刊

作者： Wang, Xiaomin 1 ; Wang, Haoriqin 1 ; Zhao, Guocheng 4 ; Liu, Zhichao 1 ; Wu, Huarui 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Inner Mongolia Univ Nationalities, Coll Comp Sci & Technol, Tongliao 028043, Peoples R China

3.Shenyang Agr Univ, Sch Informat & Elect Engn, Shenyang 110866, Peoples R China

4.China Univ Min & Technol, Sch Mech Elect & Informat Engn, Beijing 100083, Peoples R China

关键词： ALBERT; match-LSTM; natural language processing; classification; NQuAD

期刊名称：AGRONOMY-BASEL （ 影响因子：3.417； 五年影响因子：3.64 ）

ISSN：

年卷期： 2021 年 11 卷 8 期

页码：

收录情况： SCI

摘要： 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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台