survey

A Survey of Implicit Discourse Relation Recognition

Authors:

Bang WangAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 12

Article No.: 258, Pages 1 - 34

https://doi.org/10.1145/3574134

Published: 02 March 2023 Publication History

Abstract

A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple text segments, correct understanding of the theme of a discourse should take into consideration of the relations in between text segments. Although sometimes a connective exists in raw texts for conveying relations, it is more often the cases that no connective exists in between two text segments but some implicit relation does exist in between them. The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective. Indeed, the IDRR task is important to diverse downstream natural language processing tasks, such as text summarization, machine translation and so on. This article provides a comprehensive and up-to-date survey for the IDRR task. We first summarize the task definition and data sources widely used in the field. We categorize the main solution approaches for the IDRR task from the viewpoint of its development history. In each solution category, we present and analyze the most representative methods, including their origins, ideas, strengths and weaknesses. We also present performance comparisons for those solutions experimented on a public corpus with standard data processing procedures. Finally, we discuss future research directions for discourse relation analysis.

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Cited By

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A Survey of Implicit Discourse Relation Recognition

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 12

December 2023

825 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3582891

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 02 March 2023

Online AM: 07 December 2022

Accepted: 30 November 2022

Revision received: 06 August 2022

Revised: 06 August 2022

Received: 21 January 2021

Published in CSUR Volume 55, Issue 12

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https://doi.org/10.1016/j.artint.2024.104234
Lin PYang L(2024)Building a virtual psychological counselor by integrating EEG emotion detection with large-scale NLP modelsThird International Conference on Biological Engineering and Medical Science (ICBioMed2023)10.1117/12.3013169(109)Online publication date: 8-Jan-2024
https://doi.org/10.1117/12.3013169
Li QWang HYi X(2024)Learning topic emotion and logical semantic for video paragraph captioningDisplays10.1016/j.displa.2024.10270683(102706)Online publication date: Jul-2024
https://doi.org/10.1016/j.displa.2024.102706
Song WHan HHan XCheng MGong JWang SLiu T(2024)Discriminative explicit instance selection for implicit discourse relation classificationFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-3058-218:4Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s11704-023-3058-2
Xiang WLiu SWang B(2024)Parsing and encoding interactive phrase structure for implicit discourse relation recognitionNeural Computing and Applications10.1007/s00521-024-09709-836:22(13783-13797)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00521-024-09709-8
Wang RXiang WWang ZWang B(2024)Retrieval-Enhanced Template Generation for Template ExtractionNatural Language Processing and Chinese Computing10.1007/978-981-97-9431-7_29(379-391)Online publication date: 2-Nov-2024
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Zheng ZLiang CXiang WWang B(2024)Bias-Rectified Multi-way Learning with Data Augmentation for Implicit Discourse Relation RecognitionNatural Language Processing and Chinese Computing10.1007/978-981-97-9431-7_28(366-378)Online publication date: 2-Nov-2024
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Xia YLiu Y(2023)EEG-Based Emotion Recognition with Consideration of Individual DifferenceSensors10.3390/s2318774923:18(7749)Online publication date: 8-Sep-2023
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Tantos AKosmidis K(2023)From Discourse Relations to Network Edges: A Network Theory Approach to Discourse AnalysisApplied Sciences10.3390/app1312690213:12(6902)Online publication date: 7-Jun-2023
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