A minimalist approach to shallow discourse parsing and implicit relation recognition

Abstract

We describe a minimalist approach to shallow discourse parsing in the context of the CoNLL 2015 Shared Task.1 Our parser integrates a rule-based component for argument identification and data-driven models for the classification of explicit and implicit relations. We place special emphasis on the evaluation of implicit sense labeling, we present different feature sets and show that (i) word embeddings are competitive with traditional word-level features, and (ii) that they can be used to considerably reduce the total number of features. Despite its simplicity, our parser is competitive with other systems in terms of sense recognition and thus provides a solid ground for further refinement.