Abstract

This paper reports on the use of two distinct evaluation metrics for assessing a stochastic parsing model consisting of a broad-coverage Lexical-Functional Grammar (LFG), an efficient constraint-based parser and a stochastic disambiguation model. The first evaluation metric measures matches of predicate-argument relations in LFG f-structures (henceforth the LFG annotation scheme) to a gold standard of manually annotated f-structures for a subset of the UPenn Wall Street Journal treebank. The other metric maps predicate-argument relations in LFG f-structures to dependency relations (henceforth DR annotations) as proposed by Carroll et al. (Carroll et al., 1999). For evaluation, these relations are matched against Carroll et al.’s gold standard which was manually annnotated on a subset of the Brown corpus. The parser plus stochastic disambiguator gives an F-measure of 79 % (LFG) or 73 % (DR) on the WSJ test set. This shows that the two evaluation schemes are similar in spirit, although accuracy is impaired systematically by mapping one annotation scheme to the other. A systematic loss of accuracy is incurred also by corpus variation: Training the stochastic disambiguation model on WSJ data and testing on Carroll et al.’s Brown corpus data yields an F-score of 74 % (DR) for dependency-relation match. A variant of this measure comparable to the measure reported by Carroll et al. yields an F-measure of 76%. We examine divergences between annotation schemes aiming at a future improvement of methods for assessing parser quality. 1.

Citations