We present a probabilistic model extension to the Tesni`ere Dependency Structure (TDS) framework formulated in (Sangati and Mazza, 2009). This representation incorporates aspects ...
We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-...
Tahira Naseem, Harr Chen, Regina Barzilay, Mark Jo...
We examine effects that empty categories have on machine translation. Empty categories are elements in parse trees that lack corresponding overt surface forms (words) such as drop...
Adaptor grammars extend probabilistic context-free grammars to define prior distributions over trees with "rich get richer" dynamics. Inference for adaptor grammars seek...
We show that the automatically induced latent variable grammars of Petrov et al. (2006) vary widely in their underlying representations, depending on their EM initialization point...