Does Size Matter – How Much Data is Required to Train a REG Algorithm?

Theune, Mariët and Koolen, Ruud and Krahmer, Emiel and Wubben, Sander (2011) Does Size Matter – How Much Data is Required to Train a REG Algorithm? In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, 19-24 June 2011, Portland, USA.

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Abstract:	In this paper we investigate how much data is required to train an algorithm for attribute selection, a subtask of Referring Expressions Generation (REG). To enable comparison between different-sized training sets, a systematic training method was developed. The results show that depending on the complexity of the domain, training on 10 to 20 items may already lead to a good performance.
Item Type:	Conference or Workshop Item
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Human Media Interaction (HMI)
Link to this item:	http://purl.utwente.nl/publications/78517
Official URL:	http://dl.acm.org/citation.cfm?id=2002736.2002864
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