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Cited by 382

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

An unsupervised approach to recognizing discourse relations

Open access
Marcu, Daniel, Echihabi, Abdessamad·2001·Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02
DOI 10.3115/1073083.1073145
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Abstract

We present an unsupervised approach to recognizing discourse relations of CONTRAST, EXPLANATION-EVIDENCE, CONDITION and ELABORATION that hold between arbitrary spans of texts. We show that discourse relation classifiers trained on examples that are automatically extracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not explicitly marked by cue phrases.

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Metadata

Authors
Marcu, D., & Echihabi, A.
Venue
Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02
Publisher
Association for Computational Linguistics
Published
2001
Pages
368
DOI
10.3115/1073083.1073145
OpenAlex
W2152197045
Cited by
382
References
15
FWCI
4.69
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: An unsupervised approach to recognizing discourse relations. https://app.cochat.ai/papers/10.3115/1073083.1073145
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Marcu, D., & Echihabi, A. (2001). An unsupervised approach to recognizing discourse relations. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02. https://doi.org/10.3115/1073083.1073145

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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.