We present a shared logistic normal distribution as a Bayesian prior over probabilistic grammar weights. This approach generalizes the similar use of logistic normal distributions [3], enabling soft parameter tying during inference across different multinomials comprising the probabilistic grammar. We show that this model outperforms previous approaches on an unsupervised dependency grammar induction task. 1

Usually unsupervised dependency parsing tries to optimize the probability of a corpus by modifying the dependency model that was presumably used to generate the corpus. In this article we explore a different view in which a dependency structure is among other things a partial order on the nodes in terms of centrality or saliency. Under this assumption we model the partial order directly and derive dependency trees from this order. The result is an approach to unsupervised dependency parsing that is very different from standard ones in that it requires no training data. Each sentence induces a model from which the parse is read off. Our approach is evaluated on data from 12 different languages. Two scenarios are considered: a scenario in which information about part-of-speech is available, and a scenario in which parsing relies only on word forms and distributional clusters. Our approach is competitive to state-of-the-art in both scenarios. 1

A picture is worth thousand words, a small subtle gesture could be worth say much more. People have an uncanny ability to attribute mental states to others all the time, effortlessly, and mostly subconsciously. The ability to read minds is essential for operating in a complex social environment. The emerging new discipline of Network Science (the study of human behavior in relation to each other and the environment) together

A picture is worth thousand words, a small subtle gesture could be worth much more. People have an uncanny ability to attribute mental states to others all the time, effortlessly, and mostly unconsciously. The ability to read minds is essential for operating in a complex social environment. The emerging new discipline of Network Science (the study of human behavior in relation to each other and the environment) together