  We present a new parser performance prediction (PPP) model using machine
translation performance prediction system (MTPPS), statistically independent of
any language or parser, relying only on extrinsic and novel features based on
textual, link structural, and bracketing tree structural information. This new
system, MTPPS-PPP, can predict the performance of any parser in any language
and can be useful for estimating the grammatical difficulty when understanding
a given text, for setting expectations from parsing output, for parser
selection for a specific domain, and for parser combination systems. We obtain
SoA results in PPP of bracketing $F_1$ with better results over textual
features and similar performance with previous results that use parser and
linguistic label specific information. Our results show the contribution of
different types of features as well as rankings of individual features in
different experimental settings (cased vs. uncased), in different learning
tasks (in-domain vs. out-of-domain), with different training sets, with
different learning algorithms, and with different dimensionality reduction
techniques. We achieve $0.0678$ MAE and $0.85$ RAE in setting +Link, which
corresponds to about $7.4\%$ error when predicting the bracketing $F_1$ score
for the Charniak and Johnson parser on the WSJ23 test set. MTPPS-PPP system can
predict without parsing using only the text, without a supervised parser using
only an unsupervised parser, without any parser or language dependent
information, without using a reference parser output, and can be used to
predict the performance of any parser in any language.
