融合特征编码和短语交互感知的隐式篇章关系识别

王秀利; 金方焱

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融合特征编码和短语交互感知的隐式篇章关系识别

更新时间：2023-07-04

- 融合特征编码和短语交互感知的隐式篇章关系识别
- Implicit Discourse Relation Recognition Integrating Feature Coding and Phrase Interaction Perception
- 电子学报 2023年页码：1-15
- 作者机构：
  
  1.中央财经大学信息学院，北京102206
  2.国家金融安全教育部工程研究中心，北京102206
- 作者简介：
  
  [ "王秀利男，中央财经大学信息学院/国家金融安全教育部工程研究中心教授，博士生导师，主要研究方向为金融科技与安全，大数据与人工智能." ]
  [ "金方焱男，中央财经大学信息学院硕士研究生，主要研究方向为自然语言处理." ]
- 基金信息：
- DOI：
  中图分类号： TP391;
- 网络出版：2023-07-04，
- 稿件说明：
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王秀利, 金方焱. 融合特征编码和短语交互感知的隐式篇章关系识别[J/OL]. 电子学报, 2023,1-15.

WANG Xiu-li, JIN Fang-yan. Implicit Discourse Relation Recognition Integrating Feature Coding and Phrase Interaction Perception[J/OL]. ACTA ELECTRONICA SINICA, 2023, 1-15.
王秀利, 金方焱. 融合特征编码和短语交互感知的隐式篇章关系识别[J/OL]. 电子学报, 2023,1-15. DOI：

WANG Xiu-li, JIN Fang-yan. Implicit Discourse Relation Recognition Integrating Feature Coding and Phrase Interaction Perception[J/OL]. ACTA ELECTRONICA SINICA, 2023, 1-15. DOI：

摘要

隐式篇章关系识别研究因其难度大、普遍性高等特点是一项极具挑战性的任务.本文从论元编码和论元交互角度入手，提出了一种融合特征编码和短语交互感知的隐式篇章关系识别模型.该模型同时兼顾了论元本身特征和论元间交互特征的作用，并且分别进行了优化.论元编码部分整合了双向长短时记忆网络（Bidirectional Long Short-Term Memory， BiLSTM）和循环注意力卷积神经网络（Recurrent Attention Convolution Neural Network， RACNN），能够更全面地捕获论元全局和局部特征；论元交互部分从短语层级考虑论元间的语义关系建模，构建了短语级交互注意力机制，并且利用神经张量网络（Neural Tensor Network， NTN）深入挖掘其中的关系模式，更能体现出论元间潜在的更深层次的关联关系.在宾州篇章树库（Penn Discourse Treebank， PDTB）数据集上的实验结果表明，该模型F1值均优于其他对比模型.

Abstract

Implicit discourse relation recognition is a challenging task because of its difficulty and universality. From the perspective of argument coding and argument interaction

an implicit discourse relation recognition model integrating feature coding and phrase interaction perception is proposed. The model considers both the characteristics of argument itself and the interaction characteristics between arguments

and optimizes separately. The part of argument coding incorporates bidirectional long short-term memory(BiLSTM) and recurrent attention convolution neural network(RACNN)

which can capture global and local features of arguments in a more comprehensive way; in the part of argument interaction

the semantic relationship between arguments is modeled from phrase level

and a mechanism of phrase-level interactive attention is constructed. Also

neural tensor network(NTN) is used to dig into the relational pattern

which can better reflect the potential deeper relational relationship between arguments. Experimental results on penn discourse treebank(PDTB) dataset show that the F1 values of this model are superior to other comparison models.

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references

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