Most text displays an internal coherence structure, which can be analyzed as a tree structure of relations that hold between short segments of text. We present a machinelearning governed approach to such an analysis in the framework of Rhetorical Structure Theory. Our rhetorical analyzer observes a variety of textual properties, such as cue phrases, part-of-speech information, rhetorical context and lexical chaining. A two-stage parsing algorithm uses local and global optimization to find an analysis. Decisions during parsing are driven by an ensemble of support vector classifiers. This training method allows for a non-linear separation of samples with many relevant features. We define a chain of annotation tools that profits from a new underspecified representation of rhetorical structure. Classifiers are trained on a newly introduced German language corpus, as well as on a large English one. We present evaluation data for the recognition of rhetorical relations.
['David Reitter']
Simple Signals for Complex Rhetorics: On Rhetorical Analysis with Rich-Feature Support Vector Models