Ehud Reiter (reiter-e)
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Bibliography
Dale, Robert and Reiter, Ehud. 1995. “Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions.” Cognitive Science 19(2): 233–263.
Dale, Robert and Reiter, Ehud. 1996. “The Role of the Gricean Maxims in the Generation of Referring Expressions.” in AAAI-96. Working Notes: AAAI Spring Symposium on Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature, edited by Barbara di Eugenio and Nancy L. Green, pp. 16–20. Menlo Park, California: The AAAI Press.
Deemter, Kees van and Reiter, Ehud. 2019. “Lying and Computational Linguistics.” in The Oxford Handbook of Lying, edited by Jörg Meibauer, pp. 420–435. Oxford Handbooks. Oxford: Oxford University Press, doi:10.1093/oxfordhb/9780198736578.001.0001.
Marks, Joseph and Reiter, Ehud. 1990. “Avoiding Unwanted Conversational Implicatures in Text and Graphics.” in AAAI-90. Proceedings of the Eighth National Conference on Artificial Intelligence, edited by Thomas G. Dietterich and William R. Swartout, pp. 450–455. Menlo Park, California: The AAAI Press.
Mellish, Christopher S. and Reiter, Ehud. 1993. “Using Classification as a Programming Language.” in IJCAI-94. Proceedings of the 13th International Joint Conference on Artificial Intelligence, edited by Ruzena Bajcsy, pp. 696–701. San Francisco, California: Morgan Kaufmann Publishers.
Mellish, Christopher S., Reiter, Ehud and Levine, John Michael. 1996. “Natural Language Generation Applications to Technical Documentation: A View Through IDAS.” in EWNLG ’93. Trends in Natural Language Generation, An Artificial Intelligence “Perspective, Fourth European Workshop, , Pisa, Italy, April 28-30, 1993, Selected Papers, edited by Giovanni Adorni and Michael Zock, pp. 368–382. Lecture Notes in Computer Science n. 1036. Berlin: Springer.
Reiter, Ehud. 1989. “The Penman User Guide, Penman Natural Language Generation Group.” Available from USC/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, California 90292-6695. .
Reiter, Ehud. 1990. “Generating Descriptions that Exploit a User’s Domain Knowledge.” in Current Research in Natural Language Generation, edited by Robert Dale, Christopher S. Mellish, and Michael Zock, pp. 257–286. New York: Academic Press.
Reiter, Ehud. 1991a. “Nonmonotonic Reasoning.” Annual Review of Computer Science 2: 147–186. Reprinted in Shrobe (1988, 133–172).
Reiter, Ehud. 1991b. “A New Model of Lexical Choice for Nouns.” Computational Intelligence 7(4): 240–251. Special issue on Natural Language Generation.
Reiter, Ehud. 1994. “Has a Consensus NL Generation Architecture Appeared, and is it Psycholinguistically Plausible?” in INLG’94. Proceedings of the Seventh International Workshop on Natural Language Generation, edited by David D. McDonald, pp. 163–170. New Brunswick, New Jersey: Association for Computational Linguistics.
Reiter, Ehud. 1999. “Shallow vs. Deep Techniques for Handling Linguistic Constraints and Optimisations.” Unpublished manuscript, University of Aberdeen.
Reiter, Ehud. 2000. “Pipelines and Size Constraints.” Computational Linguistics 26(2): 251–259.
Reiter, Ehud. 2007. “The Shrinking Horizons of Computational Linguistics.” Computational Linguistics 33(2): 283–287.
Reiter, Ehud and Dale, E. 1997. “Building Applied Natural Language Generation Systems.” Natural Language Engineering 3: 57–88.
Reiter, Ehud and Dale, E. 2000. Building Natural Language Generation Systems. Cambridge: Cambridge University Press.
Reiter, Ehud, Levine, John Michael and Mellish, Christopher S. 1991. “Tailoring Plans to Users With Different Levels of Expertise.” dai:1948. Edinburgh: University of Edinburgh, Department of Artificial Intelligence.
Reiter, Ehud and Osman, Liesl M. 1997. “Tailored Patient Information: Some Issues and Questions.” in From Research to Commercial Applications: Making NLP Work in Practice, edited by Jill C. Burstein and Claudia Leacock, pp. 35–42. Somerset, New Jersey: Association for Computational Linguistics.
Further References
Shrobe, Howard E., ed. 1988. Exploring Artificial Intelligence. San Francisco, California: Morgan Kaufmann Publishers.