ESURF: Simple and Effective EDU Segmentation

ACL ARR 2025 February Submission4067 Authors

Abstract: Segmenting text into Elemental Discourse Units (EDUs) is a fundamental task in discourse parsing. We present a new simple method for identifying EDU boundaries, and hence segmenting them, based on lexical and character n-gram features, using random forest classification. We show that the method, despite its simplicity, outperforms other methods both for segmentation and within a state of the art discourse parser. This indicates the importance of such features for identifying basic discourse elements, pointing towards potentially more training-efficient methods for discourse analysis.

Paper Type: Short

Research Area: Discourse and Pragmatics

Research Area Keywords: EDU Segmentation, RST Parsing, Discourse parsing

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 4067