Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing
Ali Basirat, Joakim Nivre · DSpace repository (University of Tartu) · 2017
We show that a set of real-valued word vectors formed by right singular vectors of a transformed co-occurrence matrix are meaningful for determining different types of dependency relations between words. Our experimental results on the task of dependency parsing confirm the superiority of the word vectors to the other sets of word vectors generated by popular methods of word embedding. We also study the effect of using these vectors on the accuracy of dependency parsing in different languages versus using more complex parsing architectures.