ESURF: Simple and Effective EDU Segmentation

Mohammadreza Sediqin; Shlomo Engelson Argamon

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arXiv · 2501.07723

ESURF: Simple and Effective EDU Segmentation

Mohammadreza Sediqin·Shlomo Engelson Argamon

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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.

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Mohammadreza Sediqin, Shlomo Engelson Argamon. 2025-01-13. ESURF: Simple and Effective EDU Segmentation. https://arxiv.org/abs/2501.07723

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