Voted Perceptron

This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the applica- tion being the recovery of named-entity boundaries in a corpus of web data. The …...

We propose a general method for reranker construction which targets choosing the candidate with the least expected loss, rather than the most probable candidate. Different approaches to expected loss ap- …...

Following (McDonald et al., 2005), we present an application of a maximum spanning tree algorithm for a directed graph to non-projective labeled depen- dency parsing. Using a variant of the …...

Following (Blitzer et al., 2006), we present an application of structural correspondence learning to non-projective dependency pars- ing (McDonald et al., 2005). To induce the correspondences among dependency edges from …...