Cross-Domain Effects on Parse Selection for Precision Grammars

Abstract

We examine the impact of domain on parse selection accuracy, in the context of precision HPSG parsing using the English Resource Grammar, using two training corpora and four test corpora and evaluating using exact tree matches as well as dependency F-scores. In addition to determining the relative impact of in- vs. cross-domain parse selection training on parser performance, we propose strategies to avoid cross-domain performance penalty when limited in-domain data is available. Our work supports previous research showing that in-domain training data significantly improves parse selection accuracy, and that it provides greater parser accuracy than an out-of-domain training corpus of the same..