Abstract
In this chapter, we take the view that much of the existing work on the generation of referring expressions has focused on aspects of the problem that appear to be somewhat artificial when we look more closely at human-produced referring expressions. In particular, we argue that an over-emphasis on the extent to which each property in a description performs a discriminatory function has blinded us to alternative approaches to referring expression generation that might be better-placed to provide an explanation of the variety we find in human-produced referring expressions. On the basis of an analysis of a collection of such data, we propose an alternative view of the process of referring expression generation which we believe is more intuitively plausible, is a better match for the observed data, and opens the door to more sophisticated algorithms that are freed of the constraints adopted in the literature so far.
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References
Belz, A., Kow, E., Viethen, J., Gatt, A.: The GREC challenge 2008: Overview and evaluation results. In: Proceedings of the 5th International Natural Language Generation Conference, Salt Fork OH, USA, pp. 183–191 (2008)
Carletta, J.C.: Risk-taking and Recovery in Task-Oriented Dialogue. Ph.D. thesis, University of Edinburgh (1992)
Dale, R.: Cooking up referring expressions. In: Proceedings of the 27th Annual Meeting of the Association for Computational Linguistics, Vancouver BC, Canada (1989)
Dale, R., Haddock, N.: Content determination in the generation of referring expressions. Computational Intelligence 7(4), 252–265 (1991)
Dale, R., Reiter, E.: Computational interpretations of the Gricean maxims in the generation of referring expressions. Cognitive Science 19(2), 233–263 (1995)
van Deemter, K.: Generating referring expressions: Boolean extensions of the Incremental Algorithm. Computational Linguistics 28(1), 37–52 (2002)
van Deemter, K.: Generating referring expressions that involve gradable properties. Computational Linguistics 32(2), 195–222 (2006)
Gardent, C.: Generating minimal definite descriptions. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia PA, USA (2002)
Gatt, A., Belz, A., Kow, E.: The TUNA challenge 2008: Overview and evaluation results. In: Proceedings of the 5th International Natural Language Generation Conference, Salt Fork OH, USA, pp. 198–206 (2008)
Gatt, A., van Deemter, K.: Conceptual coherence in the generation of referring expressions. In: Proceedings of the 21st COLING and the 44th ACL Conference, Sydney, Australia (2006)
Horacek, H.: On referring to sets of objects naturally. In: Proceedings of the 3rd International Conference on Natural Language Generation, Brockenhurst, UK, pp. 70–79 (2004)
Jameson, A., Wahlster, W.: User modelling in anaphora generation: ellipsis and definite description. In: Proceedings of the 5th European Conference on Artificial Intelligence, Orsay, France, pp. 222–227 (1982)
Jordan, P.W.: Contextual influences on attribute selection for repeated descriptions. In: van Deemter, K., Kibble, R. (eds.) Information Sharing: Reference and Presupposition in Language Generation and Interpretation. CSLI Publications, Stanford (2002)
Kelleher, J., Kruijff, G.J.M.: Incremental generation of spatial referring expressions in situated dialog. In: Proceedings of the 21st COLING and the 44th ACL Conference, Sydney, Australia (2006)
Krahmer, E., Theune, M.: Efficient context-sensitive generation of referring expressions. In: van Deemter, K., Kibble, R. (eds.) Information Sharing: Reference and Presupposition in Language Generation and Interpretation, pp. 223–264. CSLI Publications, Stanford (2002)
Krahmer, E., van Erk, S., Verleg, A.: Graph-based generation of referring expressions. Computational Lingustics 29(1), 53–72 (2003)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
van der Sluis, I.: Multimodal Reference: Studies in Automatic Generation of Multimodal Referring Expressions. Ph.D. thesis, Tilburg University, The Netherlands (2005)
Viethen, J., Dale, R.: Algorithms for generating referring expressions: Do they do what people do? In: Proceedings of the 4th International Conference on Natural Language Generation, Sydney, Australia, pp. 63–70 (2006)
Viethen, J., Dale, R.: Generating referring expressions: What makes a difference? In: Australasian Language Technology Association Workshop 2008, Hobart, Australia, pp. 160–168 (2008)
Viethen, J., Dale, R.: The use of spatial relations in referring expression generation. In: Proceedings of the 5th International Conference on Natural Language Generation, Salt Fork OH, USA (2008)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)
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Dale, R., Viethen, J. (2010). Attribute-Centric Referring Expression Generation. In: Krahmer, E., Theune, M. (eds) Empirical Methods in Natural Language Generation. EACL ENLG 2009 2009. Lecture Notes in Computer Science(), vol 5790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15573-4_9
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DOI: https://doi.org/10.1007/978-3-642-15573-4_9
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