A computer conversational system can potentially help a foreign-language student improve his/her fluency through practice dialogues. One of its potential roles could be to correct ungrammatical sentences. This paper describes our research on a sentence-level, generation-based approach to grammar correction: first, a word lattice of candidate corrections is generated from an ill-formed input. A ...

Treebanks, such as the Penn Treebank, provide a basis for the automatic creation of broad coverage grammars. In the simplest case, rules can simply be ‘read off’ the parse-annotations of the corpus, producing either a simple or probabilistic context-free grammar. Such grammars, however, can be very large, presenting problems for the subsequent computational costs of parsing under the grammar. I...

In order to be able to capture effects from co-transcriptional folding, we extend stochastic context-free grammars such that the probability of applying a rule can depend on the length of the subword that is eventually generated from the symbols introduced by the rule, and we show that existing algorithms for training and for determining the most probable parse tree can easily be adapted to the...

We study constituent parsing of German, a morphologically rich and less-configurational language. We use a probabilistic context-free grammar treebank grammar that has been adapted to the morphologically rich properties of German by markovization and special features added to its productions. We evaluate the impact of adding lexical knowledge. Then we examine both monolingual and bilingual appr...

The present work analyzes the redundancy of sets of combinatorial objects produced by a weighted random generation algorithm proposed by Denise et al. This scheme associates weights to the terminals symbols of a weighted context-free grammar, extends this weight definition multiplicatively on words, and draws words of length n with probability proportional their weight. We investigate the level...

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 eecient 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 wh...

This paper presents a method for greatly reducing parse times in LFG by integrating a Constraint Grammar (CG) parser into a probabilistic context-free grammar. The CG parser is used in the pre-processing phase to reduce morphological and lexical ambiguity. Similarly, the c-structure pruning mechanism of XLE is used in the parsing phase to discard low-probability c-structures, before f-annotatio...

Using sentence templates and a stochastic context-free grammar a large corpus has been created, where prosodic phrase boundaries are labeled in the sentences automatically during sentence generation. 10,000 sentences of this corpus were recorded and used for our experiments. With perception experiments on 500 utterances we veriied that 92% of the automatically marked boundaries were perceived a...

In this work we propose an approach to label probabilistic synchronous context-free grammar (PSCFG) rules using only word tags, generated by either part-of-speech analysis or unsupervised word class induction. Our approach improves translation quality over the single generic label approach of Chiang (2005) on the NIST large resource Chinese-toEnglish translation task. These improvements persist...

We prove a number of useful results about probabilistic context-free grammars (PCFGs) and their Gibbs representations. We present a method, called the relative weighted frequency method, to assign production probabilities that impose proper PCFG distributions on finite parses. We demonstrate that these distributions have finite entropies. In addition, under the distributions, sizes of parses ha...