Automatic Prediction of Parser Accuracy

Statistical parsers have become increasingly accurate, to the point where they are useful in many natural language applications. However, estimating parsing accuracy on a wide variety of domains and genres is still a challenge in the absence of gold-standard parse trees. In this paper, we propose a technique that automatically takes into account certain characteristics of the domains of interest, and accurately predicts parser performance on data from these new domains. As a result, we have a cheap (no annotation involved) and effective recipe for measuring the performance of a statistical parser on any given domain.

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Sujith Ravi, Kevin Knight, Radu Soricut
• Keywords: EMNLP 2008, Gold-standard Parse Trees, Natural Language Applications, Natural Language Processing, Statistical Parser