Estimating the parameters of stochastic context-free grammars (SCFGs) from data is an important, well-studied problem. Almost without exception, existing approaches make repeated passes over the training data. The memory requirements of such algorithms are illsuited for embedded agents exposed to large amounts of training data over long periods of time. We present a novel algorithm, called HOLA...

In a previous work, a new probabilistic context-free grammar (PCFG) model for natural language parsing derived from a tree bank corpus has been introduced. The model estimates the probabilities according to a generalized k-grammar scheme for trees. It allows for faster parsing, decreases considerably the perplexity of the test samples and tends to give more structured and refined parses. Howeve...

Probabilistic context-free grammars (PCFGs) are a popular cognitive model of syntax (Jurafsky, 1996). These can be formulated to be sensitive to human working memory constraints by application of a right-corner transform (Schuler, 2009). One side-effect of the transform is that it guarantees at most a single expansion (push) and at most a single reduction (pop) during a syntactic parse. The pri...

This paper develops novel stochastic models and associated signal processing algorithms for detecting intent in terms of anomalous trajectory patterns in target tracking. Stochastic context-free grammar models are presented for anomalous trajectories using the concept of tracklets. Bayesian signal processing algorithms for each model are also described to solve the related classification and pr...

This paper presents the first empirical results to our knowledge on learning synchronous grammars that generate logical forms. Using statistical machine translation techniques, a semantic parser based on a synchronous context-free grammar augmented with λoperators is learned given a set of training sentences and their correct logical forms. The resulting parser is shown to be the bestperforming...

The application of stochastic context-free grammars to the determination of RNA foldings allows a simple description of the sub-class of sought secondary structures, but it needs efficient parsing algorithms. The more classic thermodynamic model of folding, popularized by Zuker under the framework of dynamic programming algorithms, allows an easy computation of foldings but its use is delicate ...

Translation is the task of transforming text from a given language into another. Provided with a sentence in an input language, a human translator produces a sentence in the desired target language. The advances in artificial intelligence in the 1950s led to the idea of using machines instead of humans to generate translations. Based on this idea, the field of Machine Translation (MT) was creat...

This paper proposes an approach to reduce the stochastic parsing time with stochastic context-free grammars. The basic idea consists of storing a set of precomputed problems. These precomputed problems are obtained off line from a training corpus or they are computed on line from a test corpus. In this work, experiments with the UPenn Treebank are reported in order to show the performance of bo...

This paper studies the relationship between weighted context-free grammars (WCFGs), where each production is associated with a positive real-valued weight, and probabilistic contextfree grammars (PCFGs), where the weights of the productions associated with a nonterminal are constrained to sum to one. Since the class of WCFGs properly includes the PCFGs, one might expect that WCFGs can describe ...

Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection (“what to say”) and surface realization (“how to say”) in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free g...