The DOI matches its Crossref record.
Automatic detection of text genre
Abstract
As the text databases available to users become larger and more heterogeneous, genre becomes increasingly important for computational linguistics as a complement to topical and structural principles of classification. We propose a theory of genres as bundles of facets, which correlate with various surface cues, and argue that genre detection based on surface cues is as successful as detection based on deeper structural properties.
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Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
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Riloff, E., & Wiebe, J. (2003) · Proceedings of the 2003 conference on Empirical methods in natural language processing - · doi:10.3115/1119355.1119369 · Cited by 1,059
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Vigo, M., Brown, J., & Conway, V. (2013) · Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility · doi:10.1145/2461121.2461124 · Cited by 215
Metadata
- Authors
- Kessler, B., Numberg, G., & Schütze, H.
- Venue
- Proceedings of the 35th annual meeting on Association for Computational Linguistics -
- Published
- 1997
- Pages
- 32-38
- OpenAlex
- W2146888100
- Cited by
- 358
- References
- 0
- FWCI
- 4.47
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Automatic detection of text genre. https://app.cochat.ai/papers/10.3115/976909.979622
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- Kessler, B., Numberg, G., & Schütze, H. (1997). Automatic detection of text genre. Proceedings of the 35th annual meeting on Association for Computational Linguistics -. https://doi.org/10.3115/976909.979622
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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.
