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...
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 investigate a series of targeted modifications to a data-driven dependency parser of German and show that these can be highly effective even for a relatively well studied langu...
In practical applications, decoding speed is very important. Modern structured learning technique adopts template based method to extract millions of features. Complicated templat...