article

Building applied natural language generation systems

Authors:

Robert DaleAuthors Info & Claims

Natural Language Engineering, Volume 3, Issue 1

Pages 57 - 87

https://doi.org/10.1017/S1351324997001502

Published: 01 March 1997 Publication History

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.

References

[1]

Adorni, G. and Zock, M. (eds.) (1996) Trends in Natural Language Generation: Lecture Notes in Artificial Intelligence. Berlin: Springer.

[2]

Allen, J. and Perrault, C. (1980) Analyzing intention in utterances. Artificial Intelligence 15: 143-178.

[3]

Appelt, D. (1985) Planning English referring expressions. Artificial Intelligence 26: 1-33.

Digital Library

[4]

Aust, H., Oerder, M., Seide, F. and Steinbiss, V. (1995) The Philips automatic train timetable information system. Speech Communication 17: 249-262.

Digital Library

[5]

Bateman, J. (1996) KPML Development Environment. Technical Report, IPSI, GMD, Darmstadt, Germany.

[6]

Buchanan, B., Moore, J., Forsythe, D., Carenini, G., Banks, G. and Ohlsson, S. (1995) An intelligent interactive system for delivering individualized information to patients. Artificial Intelligence in Medicine 7: 117-154.

[7]

Busemann, S. (1996) Best-first surface realization. In Proceedings of the 8th International Workshop on Natural Language Generation, pp. 101-110.

[8]

Caldwell, D. and Korelsky, T. (1994) Bilingual generation of job descriptions from quasi-conceptual forms. In Proceedings of the Fourth Conference on Applied Natural-Language Processing, pp. 1-6. Association for Computational Linguistics.

Digital Library

[9]

Cawsey, A., Binsted, K. and Jones, R. (1995) Personalised explanations for patient education. In Proceedings of the 5th European Workshop on Natural Language Generation, pp. 59-74.

[10]

Coch, J. (1996) Evaluating and comparing three text production techniques. In Proceedings of COLING-1996.

[11]

Dale, R. (1992) Generating Referring Expressions: Building Descriptions in a Domain of Objects and Processes. Cambridge: MIT Press.

[12]

Dale, R., Mellish, C. and Zock, M., editors (1990) Current Research in Natural Language Generation. London: Academic Press.

[13]

Dale, R., Hovy, E., Rösner, D. and Stock, O., editors (1992) Aspects of Automated Natural Language Generation: Lecture Notes in Artificial Intelligence. Berlin: Springer.

[14]

Dale, R. and Reiter, E. (1995) Computational interpretations of the Gricean maxims in the generation of referring expressions. Cognitive Science 19: 233-263.

[15]

Dalianis, H. and Hovy, E. (1996) Aggregation in natural language generation. In: G. Adorni and M. Zock, editors, Trends in Natural Language Generation, pp. 88-105. Berlin: Springer.

[16]

Elhadad, M. and Robin, J. (1996) An overview of SURGE: a reusable comprehensive syntactic realisation component. In Proceedings of the 8th International Workshop on Natural Language Generation (Demos and Posters), pp. 1-4.

[17]

Geldof, S. and Van de Velde, W. (1997) An architecture for template based (hyper)text generation. In Proceedings of the 6th European Workshop on Natural Language Generation, pp. 28-37.

[18]

Goldberg, E., Driedgar, N. and Kittredge, R. (1994) Using natural-language processing to produce weather forecasts. IEEE Expert 9: 45-53.

Digital Library

[19]

Goldman, N. (1975) Conceptual generation. In R. Schank (ed.), Conceptual Information Processing. New York: Elsevier.

[20]

Halliday, M. (1985) An Introduction to Functional Grammar. London: Edward Arnold.

[21]

Horacek, H. and Zock, M., editors (1993) New Concepts in Natural Language Generation. London: Pinter.

[22]

Hovy, E. (1993) Automated discourse generation using discourse structure relations. Artificial Intelligence 63: 341-386.

Digital Library

[23]

Iordanskaja, L., Kim, M., Kittredge, R., Lavoie, B. and Polguère, A. (1992) Generation of extended bilingual statistical reports. Proceedings of the Fifteenth International Conference on Computational Linguistics (COLING-92), volume 3, pp. 1019-1023.

[24]

Kasper, R. (1989) A flexible interface for linking applications to Penman's sentence generator. Proceedings of the 1989 DARPA Speech and Natural Language Workshop, pp. 153-158.

[25]

Kempen, G., editor (1987) Natural Language Generation. Martinus Nijhoff.

[26]

Kittredge, R., Korelsky, T. and Rambow, O. (1991) On the need for domain communication knowledge. Computational Intelligence 7: 305-314.

[27]

Kosslyn, S. (1994) Elements of Graphic Design. New York: W. H. Freeman.

[28]

Lavioe, B. and Rambow, O. (1997) A Fast and Portable Realizer for Text Generation Systems. Proceedings of the Fifth Conference on Applied Natural Language Processing, pp. 265-268.

[29]

Maier, E. and Hovy, E. (1993) Organising discourse structure relations using metafunctions. In: H. Horacek and M. Zock, editors, New Concepts in Natural Language Generation, pp. 69-86. London: Pinter.

[30]

Mann, W. and Thompson, S. (1988) Rhetorical structure theory: toward a functional theory of text organization. Text 3: 243-281.

[31]

Marchant, B., Cerbah, F. and Mellish, C. (1996) The Ghost Writer Project: A demonstration of the use of AI techniques in the production of technical publications. Proceedings of Expert Systems 1996: Applications Stream, pp. 9-25.

[32]

Matthiessen, C. (1991) Lexico(grammatical) choice in text generation. In: C. Paris, W. Swartout and W. Mann, editors, Natural Language Generation in Artificial Intelligence and Computational Linguistics, pp. 249-292. Kluwer Academic.

[33]

Matthiessen, C. and Bateman, J. (1991) Systemic Linguistics and Text Generation: Experiences from Japanese and English. London: Pinter.

[34]

Maybury, M. (ed.) (1993) Intelligent Multimedia Interfaces. AAAI Press.

[35]

McDonald, D. and Bolc, L., editors (1988) Natural Language Generation Systems. Berlin: Springer.

[36]

McKeown, K. (1985) Discourse strategies for generating natural-language text. Artificial Intelligence 27: 1-42.

Digital Library

[37]

McKeown, K., Kukich, K. and Shaw, J. (1994) Practical issues in automatic document generation. Proceedings of the Fourth Conference on Applied Natural-Language Processing, pp. 7-14.

Digital Library

[38]

Mel'¿uk, I. (1988) Dependency Syntax: Theory and Practice. Albany: State University of New York Press.

[39]

Moore, J. and Paris, C. (1993) Planning text for advisory dialogues: capturing intentional and rhetorical information. Computational Linguistics 19: 651-694.

Digital Library

[40]

Nogier, J-F. and Zock, M. (1992) Lexical choice as pattern-matching. Knowledge-Based Systems 5: 200-212.

Digital Library

[41]

Paris, C., Vander Linden, K., Fischer, M., Hartley, A., Pemberton, L., Power, R. and Scott, D. (1995) A support tool for writing multilingual instructions. Proceedings of Fourteenth International Joint Conference on Artificial Intelligence, pp. 1398-1404.

[42]

Paris, C., Swartout, W. and Mann, W., editors (1991) Natural Language Generation in Artificial Intelligence and Computational Linguistics. Kluwer Academic.

[43]

Pressman, R. (1994) Software Engineering: A Practitioner's Approach. McGraw-Hill.

[44]

Quirk, R. and Greenbaum, S. (1972) A University Grammar of English. Longman.

[45]

Reiter, E. (1994) Has a consensus NL generation architecture appeared, and is it psycholin-guistically plausible? Proceedings of the 7th International Workshop on Natural Language Generation, pp. 163-170.

[46]

Reiter, E., Cawsey, A., Osman, L. and Roff, Y. (1997) Knowledge acquisition for content selection. Proceedings of the 6th European Workshop on Natural Language Generation, pp. 117-126.

[47]

Reiter, E., Mellish, C. and Levine, J. (1995) Automatic generation of technical documentation. Applied Artificial Intelligence 9: 259-287.

[48]

Scott, A., Clayton, S. and Gibson, F. (1991) A Practical Guide to Knowledge Acquisition. Addison-Wesley.

[49]

Shieber, S., van Noord, G., Pereira, F. and Moore, R. (1990) Semantic head-driven generation. Computational Linguistics 16: 30-42.

Digital Library

[50]

Springer, S., Buta, P. and Wolf, T. (1991) Automatic letter composition for customer service. In Proceedings of the Innovative Applications of Artificial Intelligence Conference (CAIA-1991), pp. 67-83.

Digital Library

[51]

Stede, M. (1996) Lexical options in multilingual generation from a knowledge base. In: G. Adorni and M. Zock, editors, Trends in Natural Language Generation, pp. 222-237. Berlin: Springer.

[52]

Swartout. W. (1983) XPLAIN: a system for creating and explaining expert consulting systems. Artificial Intelligence 21: 285-325.

Digital Library

[53]

Zock, M. and Sabah, G., editors (1988) Advances in Natural Language Generation: An Interdisciplinary Perspective (two volumes). London: Pinter.

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1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
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cover image Natural Language Engineering

Natural Language Engineering Volume 3, Issue 1

March 1997

107 pages

ISSN:1351-3249

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Cambridge University Press

United States

Publication History

Published: 01 March 1997

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