Efficient Inner-to-outer Greedy Algorithm for Higher-order Labeled Dependency Parsing

Many NLP systems use dependency parsers as critical components. Jonit learn-ing parsers usually achieve better parsing accuracies than two-stage methods. How-ever, classical joint parsing algorithms significantly increase computational com-plexity, which makes joint learning im-practical. In this paper, we proposed an ef-ficient dependency parsing algorithm that is capable of capturing multiple edge-label features, while maintaining low computa-tional complexity. We evaluate our parser on 14 different languages. Our parser consistently obtains more accurate results than three baseline systems and three pop-ular, off-the-shelf parsers.