Simple Signals for Complex Rhetorics: On Rhetorical Analysis with Rich-Feature Support Vector Models
David Reitter · LDV-Forum/Journal for language technology and computational linguistics · 2003
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 machine-learning 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.