This paper describes a new grapheme-tophoneme framework, based on a combination of formal linguistic and statistical methods. A context-free grammar is used to parse words into their underlying syllable structure, and a set of subword “spellneme” units encoding both phonemic and graphemic information can be automatically derived from the parsed words. A statistical -gram model can then be train...

We study context-free grammars whose generated language is bounded (that is, subset of some expression w ̊ 1 . . . w ̊ d called bounded expression). We investigate the underlying generating process of such language and show that there exists a bounded expression u1 . . . um over the production rules, such that the language is generated only by sequences of production rules conforming to the bound...

This paper presents a coding of m-linear context-free rewriting systems (mLCFRS) into abstract categorial grammars (ACG). Thus, it shows the latter formalism, which offers a powerful grammatical framework based on a small set of computational primitives, is able to reach some interesting classes of languages w.r.t. natural language modeling.

In this paper, we propose adding long-term grammatical information in a Whole Sentence Maximun Entropy Language Model (WSME) in order to improve the performance of the model. The grammatical information was added to the WSME model as features and were obtained from a Stochastic Context-Free grammar. Finally, experiments using a part of the Penn Treebank corpus were carried out and significant i...

Learners that have better metacognition acquire knowledge faster than others who do not. If we had better models of such learning, we would be able to build a better metacognitive educational system. In this paper, we propose a computational model that uses a probabilistic context free grammar induction algorithm yielding metacognitive learning by acquiring deep features to assist future learni...

We take a multi-pass approach to machine translation decoding when using synchronous context-free grammars as the translation model and n-gram language models: the first pass uses a bigram language model, and the resulting parse forest is used in the second pass to guide search with a trigram language model. The trigram pass closes most of the performance gap between a bigram decoder and a much...

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...

We present a sentence compression system based on synchronous context-free grammars (SCFG), following the successful noisy-channel approach of (Knight and Marcu, 2000). We define a headdriven Markovization formulation of SCFG deletion rules, which allows us to lexicalize probabilities of constituent deletions. We also use a robust approach for tree-to-tree alignment between arbitrary document-a...

We develop a language model using probabilistic context-free grammars (PCFGs) that is “pseudo context-sensitive” in that the probability that a nonterminal N expands using a rule T depends on N’s parent. We give 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 PC...