We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of line...
Previous studies of data-driven dependency parsing have shown that the distribution of parsing errors are correlated with theoretical properties of the models used for learning an...
Given a parallel parsed corpus, statistical treeto-tree alignment attempts to match nodes in the syntactic trees for a given sentence in two languages. We train a probabilistic tr...
In Natural Language Processing (NLP), one key problem is how to design a robust and effective parsing system. In this paper, we will introduce a corpm- based Chinese parsing syste...
This paper proposes an approach to improve graph-based dependency parsing by using decision history. We introduce a mechanism that considers short dependencies computed in the ear...