We present an algorithm for learning context free grammars from positive structural examples (unlabeled parse trees). The algorithm receives a parameter in the form of a finite set of structures and the class of languages learnable by the algorithm depends on this parameter. Every context free language belongs to many such learnable classes. A second part of the algorithm is then used to determ...

A space-efficient approximation algorithm for the grammar-based compression problem, which requests for a given string to find a smallest context-free grammar deriving the string, is presented. For the input length n and an optimum CFG size g, the algorithm consumes only O(g log g) space and O(n log∗n) time to achieve O((log∗n) logn) approximation ratio to the optimum compression, where log∗n i...

We present a method for probabilistic parsing of German words. Our approach uses a morphological analyzer based on weighted finitestate transducers to segment words into lexical units and a probabilistic context free grammar trained on a manually created set of word trees for the parsing step.

Automatically generating program translators from source and target language specifications is a non-trivial problem. In this paperwe focus on the problem of automating the process of building translators between operations languages, a family of DSLs used to program satellite operations procedures. We exploit their similarities to semi-automatically build transformation tools between these DSL...

This thesis focus on the prediction of RNA secondary structure using stochastic context-free grammars (SCFG). The RNA secondary structure prediction problem consists of predicting a 2-dimensional structure from a 1-dimensional nucleotide sequence. The theory behind SCFG is explained and an overview of the research literature on various methods in the field of secondary structure prediction is g...

We generalize Solomonoff’s stochastic context-free grammar induction method to context-sensitive grammars, and apply it to transfer learning problem by means of an efficient update algorithm. The stochastic grammar serves as a guiding program distribution which improves future probabilistic induction approximations by learning about the training sequence of problems. Stochastic grammar is updat...

We develop a language model using probabilistic context-free grammars (PCFGs) that is \pseudo context-sensitive" in that the probability that a non-terminal N expands using a rule r depends on N's parent. We derive the equations for estimating the necessary probabilities using a variant of the inside-outside algorithm. We give experimental results showing that, beginning with a high-performance...

Context-aware applications behave differently depending on the context in which they are running. Since context-specific behaviour tends to crosscut base programs, it can advantageously be implemented as aspects. This leads to the notion of context-aware aspects, i.e., aspects whose behaviour depends on context. This paper analyzes the issue of appropriate support from the aspect language to bo...

When reasoning about reasoning, one often needs to carry out some proof steps within a diierent theory from the current one and then to lift the conclusions back into the original theory. The deductive rules required to carry out these steps involve either reeec-tion principles or some other notion of proof in context. Reeection principles have to be carefully restricted in order to avoid parad...

Nowadays, numerous journals and conferences have published articles related to context-aware systems, indicating many researchers’ interest. Therefore, the goal of this paper is to review the works that were published in journals, suggest a new classification framework of context-aware systems, and explore each feature of classification framework. This paper is based on a literature review of c...