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-List Of Titles -GRE3D7 : A Corpus of distinguishing descriptions for objects in visual scenes

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/160931

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Title
GRE3D7 : A Corpus of distinguishing descriptions for objects in visual scenes
Related
Workshop on Language Generation and Evaluation (31 July 2011 : Edinburgh)
Related
Proceedings of the UCNLG+Eval : Language Generation and Evaluation Workshop, p.12-22
Related
http://aclweb.org/anthology-new/W/W11/W11-2702.pdf
Publisher
Association for Computational Linguistics (ACL)
Date
2011
Author/Creator
Viethen, Jette
Author/Creator
Dale, Robert
Description
Recent years have seen a trend towards empirically motivated and more data-driven approaches in the field of referring expression generation (REG). Much of this work has focussed on initial reference to objects in visual scenes. While this scenario of use is one of the strongest contenders for real-world applications of referring expression generation, existing data sets still only embody very simple stimulus scenes. To move this research forward, we require data sets built around increasingly complex scenes, and we need much larger data sets to accommodate their higher dimensionality. To control the complexity, we also need to adopt a hypothesis-driven approach to scene design. In this paper, we describe GRE3D7, the largest corpus of humanproduced distinguishing descriptions available to date, discuss the hypotheses that underlie its design, and offer a number of analyses of the 4480 descriptions it contains.
Description
11 page(s)
Resource Type
conference paper
Organisation
Macquarie University. Centre for Language Technology

Identifier
http://hdl.handle.net/1959.14/160931
Identifier
ISBN:9781937284183
Identifier
mq_res-20120320-142826
Language
eng
Rights
Copyright the Publisher 2011. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
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"Proceedings of the UCNLG+Eval : Language Generation and Evaluation Workshop"
 
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