Paper: High Precision Treebanking - Blazing Useful Trees Using POS Information

ACL ID P05-1041
Title High Precision Treebanking - Blazing Useful Trees Using POS Information
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

In this paper we present a quantitative and qualitative analysis of annotation in the Hinoki treebank of Japanese, and in- vestigate a method of speeding annotation by using part-of-speech tags. The Hinoki treebank is a Redwoods-style treebank of Japanese dictionary de nition sentences. 5,000 sentences are annotated by three dif- ferent annotators and the agreement evalu- ated. An average agreement of 65.4% was found using strict agreement, and 83.5% using labeled precision. Exploiting POS tags allowed the annotators to choose the best parse with 19.5% fewer decisions.