Utilizing Dependency Language Models for Graph based Dependency Parsing Models

(2012) propose to learn cross-lingual word clusters from multilingual paralleled un- labeled data through word alignments, and ap- ply these clusters as features for

In this paper, we first describe an intuitionis- tic method for dependency parsing, which re- sorts to a classifier to determine whether a word pair forms a dependency edge, and

Feature-based integration in the sense of letting a subset of the features for one model be derived from the output of a different model has been exploited for dependency parsing

We have shown how lexical features derived from clusters and lemmas may improve data-driven dependency parsing of web data and even replace individual word forms during parsing..

This paper proposes to learn language- independent word representations to ad- dress cross-lingual dependency parsing, which aims to predict the dependency parsing trees for

We use a pipeline approach, in which syntactic dependency parsing, word sense disambiguation, and semantic role labeling are performed separately: Syn- tactic dependency parsing

The evaluation shows that labeled pseudo-projective dependency parsing, using a deterministic parsing algorithm and SVM classifiers, gives competitive parsing accuracy for all

We prove (in sketches) that our weighted parsing algorithm is terminating and solves the weighted parsing problem for every closed wRTG-LM with a finitely decomposing language