  In this paper, we present an approach to improve the accuracy of a strong
transition-based dependency parser by exploiting dependency language models
that are extracted from a large parsed corpus. We integrated a small number of
features based on the dependency language models into the parser. To
demonstrate the effectiveness of the proposed approach, we evaluate our parser
on standard English and Chinese data where the base parser could achieve
competitive accuracy scores. Our enhanced parser achieved state-of-the-art
accuracy on Chinese data and competitive results on English data. We gained a
large absolute improvement of one point (UAS) on Chinese and 0.5 points for
English.
