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.
REF propose a method to predict parser accuracy based on properties of the new domain of interest and properties of the domain on which the parser was trained.
Automatic Prediction of Parser Accuracy
Conference On Empirical Methods In Natural Language Processing
Computer Science