Unsupervised induction of dependency structures using Probabilistic Bilexical Grammars

Unsupervised parsing induction has attracted a significant amount of attention over the last few years. However, current systems exhibit a degree of complexity that can shy away newcomers to the field. We challenge the need for such complexity and present a straightforward weak-EM based system. The results we obtained are close to state-of-the-art ones while still making it extremely simple to experiment with different sub-components. We use a k-best parser, an inductor for Probabilistic Bilexical Grammars (PBGs) [1] and a simple treebank builder. Since our algorithm is independent of the PBG inductor, it overlaps with other models from the literature such as Dependency Model with Valence [2]. Our algorithms are fully fleshed and easily reproducible. We experiment in 8 languages that inform intuitions in training- size dependent parameterization.