基于Highway-BiLSTM网络的汉语谓语中心词识别研究

黄瑞章; 靳文繁; 陈艳平; 秦永彬; 郑庆华

doi:10.11959/j.issn.1000-436x.2021027

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基于Highway-BiLSTM网络的汉语谓语中心词识别研究

学术论文 | 更新时间：2024-06-05

- 基于Highway-BiLSTM网络的汉语谓语中心词识别研究
- Research on Chinese predicate head recognition based on Highway-BiLSTM network
- 通信学报 2021年42卷第1期页码：100-107
- 作者机构：
  
  1. 贵州大学计算机科学与技术学院，贵州贵阳 550025
  2. 贵州省公共大数据重点实验室，贵州贵阳 550025
  3. 西安交通大学计算机科学与技术学院，陕西西安 710049
- 作者简介：
  
  [ "黄瑞章（1979-），女，天津人，博士，贵州大学副教授，主要研究方向为数据融合分析、文本挖掘、网络挖掘和知识发现。" ]
  [ "靳文繁（1996- ），男，贵州毕节人，贵州大学硕士生，主要研究方向为自然语言处理、信息抽取。" ]
  [ "陈艳平（1980- ），男，贵州长顺人，博士，贵州大学副教授，主要研究方向为人工智能、自然语言处理。" ]
  [ "秦永彬（1980- ），男，山东招远人，博士，贵州大学教授，主要研究方向为大数据治理与应用、多源数据融合。" ]
  [ "郑庆华（1969- ），男，浙江嵊州人，博士，西安交通大学教授，主要研究方向为大数据知识工程、网络舆情监测。" ]
- 基金信息：
  
  国家自然科学基金资助项目(U1836205);国家自然科学基金资助项目(91746116);贵州省科学技术基金资助项目([2020]1Z055)
- DOI：10.11959/j.issn.1000-436x.2021027
  中图分类号： TN391
- 网络出版日期：2021-01，
  
  纸质出版日期：2021-01-25
- 稿件说明：
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黄瑞章, 靳文繁, 陈艳平, 等. 基于Highway-BiLSTM网络的汉语谓语中心词识别研究[J]. 通信学报, 2021,42(1):100-107.

Ruizhang HUANG, Wenfan JIN, Yanping CHEN, et al. Research on Chinese predicate head recognition based on Highway-BiLSTM network[J]. Journal on communications, 2021, 42(1): 100-107.
黄瑞章, 靳文繁, 陈艳平, 等. 基于Highway-BiLSTM网络的汉语谓语中心词识别研究[J]. 通信学报, 2021,42(1):100-107. DOI： 10.11959/j.issn.1000-436x.2021027.

Ruizhang HUANG, Wenfan JIN, Yanping CHEN, et al. Research on Chinese predicate head recognition based on Highway-BiLSTM network[J]. Journal on communications, 2021, 42(1): 100-107. DOI： 10.11959/j.issn.1000-436x.2021027.

摘要

针对汉语谓语中心词识别困难及唯一性的问题，提出了一种基于Highway-BiLSTM网络的深度学习模型。首先，通过多层 BiLSTM 网络叠加获取句子内部不同粒度抽象语义信息的直接依赖关系；然后，利用 Highway网络缓解深层模型出现的梯度消失问题；最后，通过约束层对输出路径进行规划，解决谓语中心词的唯一性问题。实验结果表明，该方法有效提升了谓语中心词识别的性能。

Abstract

Aiming at the problem of difficult recognition and uniqueness of Chinese predicate head

a Highway-BiLSTM model was proposed.Firstly

multi-layer BiLSTM networks were used to capture multi-granular semantic dependence in a sentence.Then

a Highway network was adopted to alleviate the problem of gradient disappearance.Finally

the output path was optimized by a constraint layer which was designed to guarantee the uniqueness of predicate head.The experimental results show that the proposed method effectively improves the performance of predicate head recognition.

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references

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