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...
Robustness to illumination variations is a key requirement for the problem of change detection which in turn is a fundamental building block for many visual surveillance applicati...
This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability estimation techniqu...
In practical applications, decoding speed is very important. Modern structured learning technique adopts template based method to extract millions of features. Complicated templat...
We present new training methods that aim to mitigate local optima and slow convergence in unsupervised training by using additional imperfect objectives. In its simplest form, lat...
Valentin I. Spitkovsky, Hiyan Alshawi, Daniel Jura...