Core Concepts
Enhanced Rhetorical Structure Theory (eRST) offers a comprehensive framework for discourse analysis, incorporating signals and secondary edges to enhance the representation of discourse relations.
Abstract
The article introduces eRST, an extension of Rhetorical Structure Theory (RST), focusing on computational discourse analysis. It addresses shortcomings in existing frameworks like SDRT and PDTB by introducing tree-breaking, non-projective relations, and explicit signals. The primary goal is to provide a detailed representation of discourse relations across various genres. The content covers the theoretical background, related work, formalism details, complexity considerations, data extraction from the GUM corpus, and practical applications.
Directory:
- Introduction to eRST
- Presents Enhanced Rhetorical Structure Theory as a new theoretical framework for computational discourse analysis.
- Data Extraction from GUM Corpus
- Extends annotations in the English Georgetown University Multilayer corpus (GUM) covering 12 genres with over 26K EDUs.
- Complexity and Effort Considerations
- Discusses the computational complexity of eRST derivations compared to RST and addresses annotation effort challenges.
- Practical Applications and Future Prospects
- Highlights the potential applications of eRST in linguistic research and computational models.
Stats
The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations.
The proposed theory addresses shortcomings in existing frameworks like SDRT and PDTB using constructs in the theory.
A corpus of over 200K tokens covering 12 spoken and written English text types is evaluated according to the framework.
Multiple concurrent relations are supported by eRST along with hierarchical relation taxonomy.
Automatic parsing is discussed along with evaluation metrics for data within the framework.