Structure informationnelle et constructions du kabyle : Etude de trois types de phrase dans le cadre de la grammaire constructionnelle

Information structure and Kabyle constructions Three sentence types in the Construction Grammar framework The study examines three Kabyle sentence types and their variants. These sentence types have been chosen because they code the same state of affairs but have different syntactic structures. The sentence types are Dislocated sentence, Cleft sentence, and Canonical sentence. I argue first that a proper description of these sentence types should include information structure and, second, that a description which takes into account information structure is possible in the Construction Grammar framework. The study thus constitutes a testing ground for Construction Grammar for its applicability to a less known language. It constitutes a testing ground notably because the differentiation between the three types of sentences cannot be done without information structure categories and, consequently, these categories must be integrated also in the grammatical description. The information structure analysis is based on the model outlined by Knud Lambrecht. In that model, information structure is considered as a component of sentence grammar that assures the pragmatically correct sentence forms. The work starts by an examination of the three sentence types and the analyses that have been done in André Martinet s functional grammar framework. This introduces the sentence types chosen as the object of study and discusses the difficulties related to their analysis. After a presentation of the state of the art, including earlier and more recent models, the principles and notions of Construction Grammar and of Lambrecht s model are introduced and explicated. The information structure analysis is presented in three chapters, each treating one of the three sentence types. The analyses are based on spoken language data and elicitation. Prosody is included in the study when a syntactic structure seems to code two different focus structures. In such cases, it is pertinent to investigate whether these are coded by prosody. The final chapter presents the constructions that have been established and the problems encountered in analysing them. It also discusses the impact of the study on the theories used and on the theory of syntax in general.

Adolescent writing development: Improving succinctness using the extended noun phrase

This study examined an aspect of adolescent writing development, specifically whether teaching secondary school students to use strategies to enhance succinctness in their essays changed the grammatical sophistication of their sentences. A quasi-experimental intervention was used to compare changes in syntactic complexity and lexical density between one-draft and polished essays. No link was demonstrated between the intervention and the changes. A thematic analysis of teacher interviews explored links between changes to student texts and teaching approaches. The study has implications for making syntactic complexity an explicit goal of student drafting.

Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.

Phrase linguistic classification and generalization for improving statistical machine translation

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed. © 2005 Association for Computational Linguistics.

Computational Structure of GPSG Models: Revised Generalized Phrase Structure Grammar

The primary goal of this report is to demonstrate how considerations from computational complexity theory can inform grammatical theorizing. To this end, generalized phrase structure grammar (GPSG) linguistic theory is revised so that its power more closely matches the limited ability of an ideal speaker--hearer: GPSG Recognition is EXP-POLY time hard, while Revised GPSG Recognition is NP-complete. A second goal is to provide a theoretical framework within which to better understand the wide range of existing GPSG models, embodied in formal definitions as well as in implemented computer programs. A grammar for English and an informal explanation of the GPSG/RGPSG syntactic features are included in appendices.