Predicting the Position of Attributive Adjectives in the French NP

Cet article est une version révisée de l'article paru dans Student session of the European Summer School for Logic, Language and Information, Copenhague : Danemark (2010)International audienceThis article proposes a quantitative study of the placement alternation for the adjective within the noun phrase in French. Taking the hypothesis that position constraints are mostly preferential as a starting point, we develop a methodology based on statistical inference in order to provide a formal account of the relative importance of different groups of constraints. Results show the relative importance of lexical constraints and that frequency-based and length constraints are the best predictors. This suggests that the placement of adjectives not only depends on our knowledge of lexical items but also on the knowledge of the way in which we use them in discourse, i.e. on usage

A comparative study of algorithms for solving seemingly unrelated regressions models

The computational efficiency of various algorithms for solving seemingly unrelated regressions (SUR) models is investigated. Some of the algorithms adapt known methods; others are new. The first transforms the SUR model to an ordinary linear model and uses the QR decomposition to solve it. Three others employ the generalized QR decomposition to solve the SUR model formulated as a generalized linear least-squares problem. Strategies to exploit the structure of the matrices involved are developed. The algorithms are reconsidered for solving the SUR model after it has been transformed to one of smaller dimensions