Attribute-centric referring expression generation

Robert Dale*, Jette Viethen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationEmpirical Methods in Natural Language Generation
Subtitle of host publicationData-Oriented Methods and Empirical Evaluation
EditorsEmiel Krahmer, Mariët Theune
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Number of pages17
ISBN (Electronic)9783642155734
ISBN (Print)3642155723, 9783642155727
Publication statusPublished - 2010
Event12th European Workshop on Natural Language Generation, ENLG 2009 - Athens, Greece
Duration: 30 Mar 20093 Apr 2009

Publication series

NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)16113-349


Other12th European Workshop on Natural Language Generation, ENLG 2009


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