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Sentence level discourse parsing using syntactic and lexical information

Open access
Soricut, Radu, Marcu, Daniel·2003·Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03

Abstract

We introduce two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees. The models use syntactic and lexical features. A discourse parsing algorithm that implements these models derives discourse parse trees with an error reduction of 18.8% over a state-of-the-art decision-based discourse parser. A set of empirical evaluations shows that our discourse parsing model is sophisticated enough to yield discourse trees at an accuracy level that matches near-human levels of performance.

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Metadata

Authors
Soricut, R., & Marcu, D.
Venue
Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03
Published
2003
Volume
1
Pages
149-156
OpenAlex
W2154407881
Cited by
402
References
2
FWCI
7.61
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: Sentence level discourse parsing using syntactic and lexical information. https://app.cochat.ai/papers/10.3115/1073445.1073475
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Soricut, R., & Marcu, D. (2003). Sentence level discourse parsing using syntactic and lexical information. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03. https://doi.org/10.3115/1073445.1073475

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.