Do Automatic Annotation Techniques Have Any Impact on Supervised Complex Question Answering?

Paper: Do Automatic Annotation Techniques Have Any Impact on Supervised Complex Question Answering?

ACL ID	P09-2083
Title	Do Automatic Annotation Techniques Have Any Impact on Supervised Complex Question Answering?
Venue	Annual Meeting of the Association of Computational Linguistics
Session	Short Paper
Year	2009
Authors	Yllias Chali (University of Lethbridge, Lethbridge AB) Sadid Hasan (University of British Columbia, Vancouver BC) Shafiq R. Joty

In this paper, we analyze the impact of different automatic annotation methods on the performance of supervised approaches to the complex question answering prob- lem (defined in the DUC-2007 main task). Huge amount of annotated or labeled data is a prerequisite for supervised train- ing. The task of labeling can be ac- complished either by humans or by com- puter programs. When humans are em- ployed, the whole process becomes time consuming and expensive. So, in order to produce a large set of labeled data we prefer the automatic annotation strategy. We apply five different automatic anno- tation techniques to produce labeled data using ROUGE similarity measure, Ba- sic Element (BE) overlap, syntactic sim- ilarity measure, semantic similarity mea- sure, and Extended String Subsequence Ker...

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