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Using a Wikipedia-based Semantic Relatedness Measure for Document Clustering
written by Stephen Tratz and Eduard Hovy (Information Sciences Institute, University of Southern Carolina)
spoken by Martin Popel
reported by Michal Novák
Introduction
The paper describes a high-quality conversion of Penn Treebank to dependency trees. The authors introduce an improved labeled dependency scheme based on the Stanford's one. In addition, they extended the non-directional easy-first first algorithm of Goldberg and Elhadad to support non-projective trees by adding “move” actions inspired by Nivre's swap-based reordering for shift-reduce parsing.