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

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...