Pure statistical parsing systems achieves high in-domain accuracy but performs poorly out-domain.

With the extra features, we hope that the training of the statistical model will not overfit the in-domain data, but be able to deal with domain independent linguistic phenomena as well.

With both parsers, we see slight performance drops with both HP SG feature models on in-domain tests (WSJ), compared with the original models.

When we look at the performance difference between in-domain and out-domain tests for each feature model, we observe that the drop is significantly smaller for the extended models with HP SG features.

Admittedly the results on PCHEMTB are lower than the best reported results in CoNLL 2007 Shared Task, we shall note that we are not using any in-domain unlabeled data.

Listed together with their PARSEVAL F-measures these are: gold-standard parses from the treebank (GoldSyn, 100%), a parser trained on WSJ plus a small number of in-domain training sentences required to achieve good performance, 20 for CLANG (Syn20, 88.21%) and 40 for GEOQUERY (Syn40, 91.46%), and a parser trained on no in-domain data (Syn0, 82.15% for CLANG and 76.44% for GEOQUERY).

ones trained on more in-domain data) improved our approach.

Table 5: Performance on GEO25 0 (20 in-domain sentences are used in SYN20 to train the syntactic parser).