We develop the syntactic topic model (STM), a nonparametric Bayesian model of parsed documents. The STM generates words that are both thematically and syntactically constrained, w...
Neural probabilistic language models (NPLMs) have been shown to be competitive with and occasionally superior to the widely-used n-gram language models. The main drawback of NPLMs...
In this paper, we present an automated, quantitative, knowledge-poor method to evaluate the randomness of a collection of documents (corpus), with respect to a number of biased pa...
This paper presents the evaluation of a non-blocking, decoupled memory/execution, multithreaded architecture known as the Scheduled Dataflow (SDF). The major recent trend in digit...
A modular parallel distributed processing architecture for parsing, representing and paraphrasing sentences with multiple hierarchical relative clauses is presented. A lowel-level...