We investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically design...
Daniel Cer, Marie-Catherine de Marneffe, Daniel Ju...
This paper presents a freely available evaluation tool for dependency parsing, MaltEval (http://w3.msi.vxu.se/users/jni/malteval). It is flexible and extensible, and provides func...
This paper introduces a Maximum Entropy dependency parser based on an efficient kbest Maximum Spanning Tree (MST) algorithm. Although recent work suggests that the edge-factored ...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
We present a novel transition system for dependency parsing, which constructs arcs only between adjacent words but can parse arbitrary non-projective trees by swapping the order o...