The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Building applied natural language generation systems
Abstract
In this article, we give an overview of Natural Language Generation (NLG) from an applied system-building perspective. The article includes a discussion of when NLG techniques should be used; suggestions for carrying out requirements analyses; and a description of the basic NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Throughout, the emphasis is on established techniques that can be used to build simple but practical working systems now. We also provide pointers to techniques in the literature that are appropriate for more complicated scenarios.
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Cites
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Analyzing intention in utterances
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Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions
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Self-citationXPLAIN: a system for creating and explaining expert consulting programs
Swartout, W. R. (1983) · Artificial Intelligence · doi:10.1016/s0004-3702(83)80014-9 · Cited by 393
Current Research in Natural Language Generation
Lessard, G., Dale, R., Mellish, C., & Zock, M. (1992) · Language · doi:10.2307/416388 · Cited by 348
Self-citationIntelligent multimedia interfaces
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Cited by
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776
Spoken dialogue technology: enabling the conversational user interface
McTear, M. F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505285 · Cited by 571
Challenges in Data-to-Document Generation
Wiseman, S., Shieber, S., & Rush, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1239 · Cited by 524
Good Question! Statistical Ranking for Question Generation
Heilman, M., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473390 · Cited by 366
Metadata
- Authors
- REITER, E., & DALE, R.
- Venue
- Natural Language Engineering
- Publisher
- Cambridge University Press (CUP)
- Published
- 1997-03
- Volume
- 3
- Issue
- 1
- Pages
- 57-87
- ISSN
- 1351-3249 (ISSN-L)
- 1469-8110
- OpenAlex
- W1985610876
- Cited by
- 631
- References
- 31
- FWCI
- 13.79
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
