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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (2): 44-49.doi: 10.6040/j.issn.1672-3961.0.2019.313

• 机器学习与数据挖掘 • 上一篇    下一篇

一种基于深度神经网络的句法要素识别方法

陈艳平1,2(),冯丽1,3,*(),秦永彬1,2,黄瑞章1,2   

  1. 1. 贵州大学计算机科学与技术学院，贵州 贵阳550025
    2. 数据融合与分析实验室(贵州大学)，贵州 贵阳550025
    3. 贵州省智能人机交互工程技术研究中心，贵州 贵阳550025
  • 收稿日期:2019-06-16 出版日期:2020-04-20 发布日期:2020-04-16
  • 通讯作者: 冯丽 E-mail:ypench@gmail.com;gzu_fl931126@163.com
  • 作者简介:陈艳平(1980—)，男，贵州安顺人，博士，副教授，主要研究方向为数据融合分析，自然语言处理，知识发现.E-mail: ypench@gmail.com
  • 基金资助:
    国家自然科学基金联合基金重点项目(U1836205);国家自然科学基金重大研究计划项目(91746116);贵州省重大应用基础研究项目(黔科合JZ字[2014]2001);贵州省科技重大专项计划(黔科合重大专项字[2017]3002);贵州省自然科学基金(黔科合基础[2018]1035)

A syntactic element recognition method based on deep neural network

Yanping CHEN1,2(),Li FENG1,3,*(),Yongbin QIN1,2,Ruizhang HUANG1,2   

  1. 1. School of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China
    2. Data Fusion and Analysis Laboratory (Guizhou University), Guiyang 550025, Guizhou, China
    3. Guizhou Intelligent Human-Computer Interaction Engineering Technology Research Center, Guiyang 550025, Guizhou, China
  • Received:2019-06-16 Online:2020-04-20 Published:2020-04-16
  • Contact: Li FENG E-mail:ypench@gmail.com;gzu_fl931126@163.com
  • Supported by:
    国家自然科学基金联合基金重点项目(U1836205);国家自然科学基金重大研究计划项目(91746116);贵州省重大应用基础研究项目(黔科合JZ字[2014]2001);贵州省科技重大专项计划(黔科合重大专项字[2017]3002);贵州省自然科学基金(黔科合基础[2018]1035)

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摘要：

为改进传统特征方法很难获取中文句子中结构信息的问题，提出一种基于深度神经网络的句法要素识别模型。采用Bi-LSTM网络从原始数据中自动抽取句子中的结构信息和语义信息，利用Attention机制自动计算抽象语义特征的分类权重，通过CRF层对输出标签进行约束，输出最优的标注序列。经过对比验证，该模型能有效识别句子中的句法要素，在标注数据集上F1达到84.85%。

关键词: 句法要素, 信息抽取, 深度神经网络

Abstract:

It was difficult to obtain structural information in Chinese sentences by the traditional feature method. To solve the problem, according to characteristics of Chinese sentence, a Bi-LSTM-Attention-CRF model was proposed based on deep neural network. A Bi-LSTM network was used to automatically extract structural information and semantic information from raw input sentences. Attention mechanism was adopted to weight abstract semantic features for classification. An optimized label sequence was output through the CRF layer. Comparing with other methods, our model could effectively identify syntactic elements in sentences. The performance reached to 84.85% in F1 score in the evaluation data sets.

Key words: syntactic elements, information extraction, deep neural network

中图分类号: 

  • TP391

图1

Bi-LSTM-Attention-CRF句法要素识别模型"

表1

模型性能"

Model All Type SUB ADV RAI LOC TEM
P/% R/% F1/% P/% R/% F1/% P/% R/% F1/% P/% R/% F1/% P/% R/% F1/% P/% R/% F1/%
CRF 86.61 80.34 83.36 83.75 72.93 77.97 74.35 66.06 69.96 85.79 80.21 82.91 84.33 75.30 79.56 78.61 71.20 74.73
Bi-LSTM-CRF 86.25 83.06 84.62 82.14 79.66 80.88 70.49 68.47 69.46 89.39 83.69 86.45 84.15 73.60 78.52 81.77 78.17 79.92
Bi-LSTM-Attention-CRF 86.22 83.52 84.85 82.19 78.74 80.43 71.88 71.00 71.44 87.65 81.97 87.71 86.23 86.80 86.51 81.87 75.41 78.51
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