  We develop novel first- and second-order features for dependency parsing
based on the Google Syntactic Ngrams corpus, a collection of subtree counts of
parsed sentences from scanned books. We also extend previous work on surface
$n$-gram features from Web1T to the Google Books corpus and from first-order to
second-order, comparing and analysing performance over newswire and web
treebanks.
  Surface and syntactic $n$-grams both produce substantial and complementary
gains in parsing accuracy across domains. Our best system combines the two
feature sets, achieving up to 0.8% absolute UAS improvements on newswire and
1.4% on web text.
