  Syntactic parsing using dependency structures has become a standard technique
in natural language processing with many different parsing models, in
particular data-driven models that can be trained on syntactically annotated
corpora. In this paper, we tackle transition-based dependency parsing using a
Perceptron Learner. Our proposed model, which adds more relevant features to
the Perceptron Learner, outperforms a baseline arc-standard parser. We beat the
UAS of the MALT and LSTM parsers. We also give possible ways to address parsing
of non-projective trees.
