Implicit Discourse Relation Recognition Integrating Feature Coding and Phrase Interaction Perception

WANG Xiu-li; JIN Fang-yan

doi:10.12263/DZXB.20220246

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Implicit Discourse Relation Recognition Integrating Feature Coding and Phrase Interaction Perception

PAPERS | 更新时间：2025-12-08

- Implicit Discourse Relation Recognition Integrating Feature Coding and Phrase Interaction Perception
- ACTA ELECTRONICA SINICA Vol. 52, Issue 4, Pages: 1377-1388(2024)
- 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：
- 基金信息：
  
  Key Projects of Philosophy and Social Sciences Research, Ministry of Education PR China(22JZD011);Emerging Interdisciplinary Project of CUFE
- DOI：10.12263/DZXB.20220246
  CLC： TP391;
- Received：09 March 2022，
  
  Revised：2022-09-14，
  
  Published：25 April 2024
- 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]. 电子学报, 2024, 52(04): 1377-1388.

WANG Xiu-li, JIN Fang-yan. Implicit Discourse Relation Recognition Integrating Feature Coding and Phrase Interaction Perception[J]. Acta Electronica Sinica, 2024, 52(04): 1377-1388.
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]. 电子学报, 2024, 52(04): 1377-1388. DOI：10.12263/DZXB.20220246

WANG Xiu-li, JIN Fang-yan. Implicit Discourse Relation Recognition Integrating Feature Coding and Phrase Interaction Perception[J]. Acta Electronica Sinica, 2024, 52(04): 1377-1388. DOI：10.12263/DZXB.20220246

摘要

隐式篇章关系识别难度大、普遍性高.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.该模型兼顾了论元本身特征和论元间交互特征的作用，并分别进行了优化.论元编码部分整合了双向长短时记忆网络和循环注意力卷积神经网络，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，并利用神经张量网络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.在宾州篇章树库数据集上的实验结果表明，该模型

值均优于其他模型.

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

values of this model are superior to other comparison models.

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references

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