Experiments show that web-scale data improves statistical dependency parsing, particularly for long dependency relationships .

The results show that web-scale data improves the dependency parsing, particularly for long dependency relationships .

The results here show that the proposed approach improves the dependency parsing performance, particularly for long dependency relationships .

The results show that web-derived selectional preference can improve the statistical dependency parsing, particularly for long dependency relationships .

Our research, however, applies the web-scale data (Google hits and Google V1) to model the word-to-word dependency relationships rather than compound bracketing disambiguation.

Our approach, however, extends these techniques to dependency parsing, particularly for long dependency relationships , which involves more challenging tasks than the previous work.

We also used the Kyoto University Text Corpus4 that provides dependency relations information for the same articles as the NAIST Text Corpus.

To create a subject detection model for Italian, we used the TUT corpus9 (Bosco et al., 2010), which contains manually annotated dependency relations and their labels, consisting of 80,878 tokens in CoNLL format.

We induced an maximum entropy classifier by using as items all arcs of dependency relations , each of which is used as a positive instance if its label is subject; otherwise it is used as a negative instance.