Grammatical evolution (GE) is one of the newest among computational methods (Ryan et al., 1998), (O’Neill and Ryan, 2001). Basically, it is a tool used to automatically generate Backus-Naur-Form (BNF) computer programmes. The method's evolution mechanism may be based on a standard genetic algorithm (GA). GE is very often used to solve the problem of a symbolic regression, determining a module's...

This study investigated the processing of lexical stress in native and non-native speakers of English. Specifically, we examined stress typicality effects (where typicality is defined on the basis of grammatical category) in disyllabic words using two on-line tasks. Our grammatical classification experiment showed an overall effect of stress typicality in non-native speakers but no overall effe...

This study investigates the usefulness of the Treebank of Learner English (TLE) when applied to the task of Native Language Identification (NLI). The TLE is effectively a parallel corpus of Standard/Learner English, as there are two versions; one based on original learner essays, and the other an error-corrected version. We use the corpus to explore how useful a parser trained on ungrammatical ...

The paper describes a set of techniques developed for discrete dictation within a vocabulary that contains up to a million entries, which is one of the main challenges in highly inflected languages like Czech. We present our approach to building an efficiently coded tree lexicon with suffix sub-trees and morphologic classification. Acoustic modeling is based on either monophone, diphone, or tri...

This study investigates the usefulness of the Treebank of Learner English (TLE) when applied to the task of Native Language Identification (NLI). The TLE is effectively a parallel corpus of Standard/Learner English, as there are two versions; one based on original learner essays, and the other an error-corrected version. We use the corpus to explore how useful a parser trained on ungrammatical ...

Grammar checking and correction comprise of the primary problems in the area of Natural Language Processing (NLP). Traditional approaches fall into two major categories: Rule based and Corpus based. While the former relies heavily on grammar rules the latter approach is statistical in nature. We provide a novel corpus based approach for grammar checking that uses the principles of an Artificial...

In automatic speech recognition, the standard choice for a language model is the well-known n-gram model. The n-grams are used to predict the probability of a word given its n-1 preceding words. However, the n-gram model is not able to explicitly learn grammatical relations of the sentence. In the present work, in order to augment the n-gram model with grammatical features, we apply the Whole S...

The current research demonstrates a system inspired by cognitive neuroscience and developmental psychology that learns to construct mappings between the grammatical structure of sentences and the structure of their meaning representations. Sentence to meaning mappings are learned and stored as grammatical constructions. These are stored and retrieved from a construction inventory based on the c...

Current methods for automatically evaluating grammatical error correction (GEC) systems rely on gold-standard references. However, these methods suffer from penalizing grammatical edits that are correct but not in the gold standard. We show that reference-less grammaticality metrics correlate very strongly with human judgments and are competitive with the leading reference-based evaluation metr...

Appears in Computational Natural Language Learning (CoNLL-99), pages 43-52. A workshop at the 9th Conf. of the European Chapter of the Assoc. for Computational Linguistics (EACL-99). Bergen, Norway, June, 1999. cs.CL/9906015 Grammatical relationships are an important level of natural language processing. We present a trainable approach to find these relationships through transformation sequence...