Greedy Universal Dependency Parsing with Right Singular Word Vectors
Ali Basirat, Joakim Nivre · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2016
A set of continuous feature vectors formed by right singular vectors of a transformed co-occurrence matrix are used with the Stanford neural dependency parser to train parsing models for a limited number of languages in the corpus of universal dependencies. We show that the feature vector can help the parser to remain greedy and be as accurate as (or even more accurate than) some other greedy and non-greedy parsers.