This thesis studies parsing and literature with the Data-Oriented Parsing framework, which assumes that chunks of previous experience can be exploited to analyze new sentences. As chunks we consider syntactic tree fragments. After presenting a method to efficiently extract such fragments from treebanks based on heuristics of re-occurrence, we employ them to develop a multi-lingual statistical parser. We show how a mildly context-sensitive grammar can be employed to produce discontinuous constituents, and compare this to an approximation that stays within the efficiently parsable context-free framework. We show that tree fragments allow the grammar to adequately capture the statistical regularities of non-local relations, without the need for the increased generative capacity of mildly context-sensitive grammar. The second part investigates what separates literary from other novels. We work with a corpus of novels and a reader survey with ratings of how literary they are perceived to be. The main goal is to find out the extent to which the literary ratings can be predicted from the texts. We first evaluate simple measures such as vocabulary richness, text compressibility, and the number of cliché expressions. In addition we apply more sophisticated, predictive models: a topic model, bag-of-words model, and a model based on syntactic tree fragments. We find that literary ratings are predictable from textual features to a large extent. While it is not possible to infer a causal relation, this result clearly rules out the notion that these value-judgments of literary merit were arbitrary, or predominantly determined by factors beyond the text.