In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of his...

A new method of discovering the common secondary structure of a family of homologous RNA sequences using Gibbs sampling and stochastic context-free grammars is proposed. Given an unaligned set of sequences, a Gibbs sampling step simultaneously estimates the secondary structure of each sequence and a set of statistical parameters describing the common secondary structure of the set as a whole. T...

Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context information and adapting systems behavior accordingly. Several context models have been defined and various context-aware middleware has been developed in order to simplify the development of context-aware applications. Unfortunately, the development of an application by usin...

We describe a corpus-based induction algorithm for probabilistic context-free grammars. The algorithm employs a greedy heuristic search within a Bayesian framework, and a post-pass using the InsideOutside algorithm. We compare the performance of our algorithm to n-gram models and the Inside-Outside algorithm in three language modeling tasks. In two of the tasks, the training data is generated b...

This paper proposes a data-driven method for concept-to-text generation, the task of automatically producing textual output from non-linguistic input. A key insight in our approach is to reduce the tasks of content selection (“what to say”) and surface realization (“how to say”) into a common parsing problem. We define a probabilistic context-free grammar that describes the structure of the inp...

We show that under certain conditions, a language model can be trained on the basis of a second language model. The main instance of the technique trains a finite automaton on the basis of a probabilistic context-free grammar, such that the Kullback-Leibler distance between grammar and trained automaton is provably minimal. This is a substantial generalization of an existing algorithm to train ...

We propose an automatic analyzer for acquiring a time-span tree based on the generative theory of tonal music (GTTM). Although analyzer based on GTTM was previously proposed, it requires manually tweaking the 46 adjustable parameters on a computer screen in order to analyze them properly. We reformalized the time-span reduction in GTTM based on a statistical model called probabilistic context-f...

We propose an online generative algorithm to enhance musical expression via intelligent improvisation accompaniment. Our framework called the ImprovGenerator, takes a live stream of percussion patterns and generates an improvised accompaniment track in real-time to stimulate new expressions in the improvisation. We use a mixture model to generate an accompaniment pattern, that takes into accoun...

In this paper, a method for prosodic break modelling based on segmental-HMMs and Dempster-Shafer fusion for speech synthesis is presented, and the relative importance of linguistic and metric constraints in prosodic break modelling is assessed . A context-dependent segmental-HMM is used to explicitly model the linguistic and the metric constraints. Dempster-Shafer fusion is used to balance the ...