The fundamental goal of this dissertation is to establish that deep, efficient, accurate parsing models can be acquired for Chinese, through parsers founded on Combinatory Categorial Grammar (CCG), a grammar formalism which has already enabled the creation of rich parsing models for English. We harness these CCG analyses of cross-linguistic syntax, harmonising them with modern accounts from Chinese generative syntax, contributing the first analysis of Chinese syntax through CCG in the literature. Supervised statistical parsing approaches rely on the availability of large annotated corpora. To avoid the cost of manual annotation, we adopt the corpus conversion methodology, in which an automatic corpus conversion algorithm projects annotations from a source corpus into the target formalism. The central contribution of this thesis is Chinese CCGbank, a corpus of 750,000 words automatically extracted from the Penn Chinese Treebank, reifying the abstract analysis through corpus conversion. We then take three state-of-the-art CCG parsers from the literature — the split-merge PCFG parser of Petrov and Klein, the transition-based CCG parser of Zhang et al., and the maximum entropy parser of Clark and Curran — and train and evaluate all three on Chinese CCGbank, achieving the first Chinese CCG parsing models in the literature. We demonstrate that while the three parsers are only separated by a small margin trained on English CCGbank, a substantial gulf of 4.8% separates the same parsers trained on Chinese CCGbank. We also confirm that the gap between the states-of-the-art in English and Chinese PSG parsing can be observed in CCG parsing. Our parsing experiments establish Chinese CCG parsing as a new and substantial challenge, a line of empirical investigation directly enabled by Chinese CCGbank.