eRST: A Signaled Graph Theory of Discourse Relations and Organization

被引:0
作者
Zeldes, Amir [1 ]
Aoyama, Tatsuya [1 ]
Liu, Yang Janet [2 ]
Peng, Siyao [2 ,5 ]
Das, Debopam [3 ]
Gessler, Luke [4 ,5 ,6 ]
机构
[1] Georgetown Univ, Dept Linguist, Washington, DC 20057 USA
[2] Ludwig Maximilians Univ Munchen, Ctr Informat & Language Proc, MaiNLP, Munich, Germany
[3] Abo Akad Univ, Dept English Language & Literature, Turku, Finland
[4] Indiana Univ, Dept Linguist, Bloomington, IN USA
[5] Georgetown Univ, Washington, DC USA
[6] Univ Colorado, Boulder, CO USA
关键词
CORPUS;
D O I
10.1162/coli_a_00538
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations, as well as implicit and explicit signals which give explainable rationales to our analyses. We survey shortcomings of RST and other existing frameworks, such as Segmented Discourse Representation Theory, the Penn Discourse Treebank, and Discourse Dependencies, and address these using constructs in the proposed theory. We provide annotation, search, and visualization tools for data, and present and evaluate a freely available corpus of English annotated according to our framework, encompassing 12 spoken and written genres with over 200K tokens. Finally, we discuss automatic parsing, evaluation metrics, and applications for data in our framework. © 2024 Association for Computational Linguistics. Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.
引用
收藏
页码:23 / 72
页数:50
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