In this paper, we present a novel approach which incorporates the web-derived selec- tional preferences to improve statistical de- pendency parsing. Conventional selectional preference learning methods have usually fo- cused on word-to-class relations, e.g., a verb selects as its subject a given nominal class. This paper extends previous work to word- to-word selectional preferences by using web- scale data. Experiments show that web-scale data improves statistical dependency pars- ing, particularly for long dependency relation- ships. There is no data like more data, perfor- mance improves log-linearly with the number of parameters (unique N-grams). More impor- tantly, when operating on new domains, we show that using web-derived selectional pref- erences is essential for achieving robust per- formance.