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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.07723 |
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| _version_ | 1866912187530870784 |
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| author | Sediqin, Mohammadreza Argamon, Shlomo Engelson |
| author_facet | Sediqin, Mohammadreza Argamon, Shlomo Engelson |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07723 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ESURF: Simple and Effective EDU Segmentation Sediqin, Mohammadreza Argamon, Shlomo Engelson Computation and Language Machine Learning 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. |
| title | ESURF: Simple and Effective EDU Segmentation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2501.07723 |