Programmers rely on programming idioms, design patterns, and workaround techniques to make up for missing programming language support. Evolving languages often address frequently encountered problems by adding language and library support to subsequent releases. By using new features, programmers can express their intent more directly. As new concerns, such as parallelism or security, arise, e...

Information source extension is utilized to improve the language model for large vocabulary continuous speech recognition (LVCSR). McMillan's theory, source extension make the model entropy close to the real source entropy, implies that the better language model can be obtained by source extension (making new unit through word concatenations and using the new unit for the language modeling). In...

The analysis of model transformations is a challenging research area within model driven engineering. Triple graph grammars (TGGs) have been applied in various transformation scenarios and their formal foundation has been a vital ground for general results concerning notions of correctness and completeness. This paper addresses existing gaps between practical scenarios and the formal results of...

Statistical machine translation (SMT) requires a parallel corpus between the source and target languages. Although a pivot-translation approach can be applied to a language pair that does not have a parallel corpus directly between them, it requires both source–pivot and pivot–target parallel corpora. We propose a novel approach to apply SMT to a resource-limited source language that has no par...

Compilers for modern object-oriented programming languages generate code in a platform independent intermediate language [LY99, CLI06] preserving the concepts of the source language; for example, classes, fields, methods, and virtual or static dispatch can be directly identified within the intermediate code. To execute this intermediate code, state-of-the-art implementations of virtual machines...

Extensible languages have a base language that can be extended incrementally with new language extensions, forming a stack with high-level languages on top and lower level languages at the bottom. Programs written with these languages are usually a mixture of code using base language and several language extensions. These extensions come with generators that translate higher level language cons...

Statistical machine translation (SMT) requires a parallel corpus between the source and target languages. Although a pivot-translation approach can be applied to a language pair that does not have a parallel corpus directly between them, it requires both source–pivot and pivot–target parallel corpora. We propose a novel approach to apply SMT to a resource-limited source language that has no par...

We consider how to edit strings from a source language so that the edited strings belong to a target language, where the languages are given as deterministic finite automata. Non-streaming (or offline) transducers perform edits given the whole source string. We show that the class of deterministic one-pass transducers with registers along with increment and min operation suffices for computing ...

The Natural Language Toolkit is a suite of program modules, data sets, tutorials and exercises, covering symbolic and statistical natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past three years, NLTK has become popular in teaching and research. We describe the toolkit and report on its current state of development.

Machine translation of a source language sentence involves selecting appropriate target language words and ordering the selected words to form a well-formed target language sentence. Most of the previous work on statistical machine translation relies on (local) associations of target words/phrases with source words/phrases for lexical selection. In contrast, in this paper, we present a novel ap...