The Object Agreement Restriction

[EN]This paper deals with the so-called Person Case Constraint (Bonet, 1991), a universal constraint blocking accusative clitics and object agreement morphemes other than third person when a dative is inserted in the same clitic/agreement cluster. The aim of this paper is twofold. First, we argue that the scope of the PCC is considerably broader than assumed in previous work, and that neither its formulation in terms of person (1st/2nd vs. 3rd)-case (accusative vs. dative) restrictions nor its morphological nature are part of the right descriptive generalization.We present evidence (i) that the PCC is triggered by the presence of an animacy feature in the object’s agreement set; (ii) that it is not case dependent, also showing up in languages that lack dative case; and (iii) that it is not morphologically bound. Second, we argue that the PCC, even if it is modified accordingly, still puts together two different properties of the agreement system that should be set apart: (i) a cross linguistic sensitivity of object agreement to animacy and (ii) a similarly widespread restriction on multiple object agreement observed crosslinguistically. These properties lead us to propose a new generalization, the Object Agreement Constraint (OAC): if the verbal complex encodes object agreement, no other argument can be licensed through verbal agreement.

Study of an object recognition algorithm

This project introduces an improvement of the vision capacity of the robot Robotino operating under ROS platform. A method for recognizing object class using binary features has been developed. The proposed method performs a binary classification of the descriptors of each training image to characterize the appearance of the object class. It presents the use of the binary descriptor based on the difference of gray intensity of the pixels in the image. It shows that binary features are suitable to represent object class in spite of the low resolution and the weak information concerning details of the object in the image. It also introduces the use of a boosting method (Adaboost) of feature selection al- lowing to eliminate redundancies and noise in order to improve the performance of the classifier. Finally, a kernel classifier SVM (Support Vector Machine) is trained with the available database and applied for predictions on new images. One possible future work is to establish a visual servo-control that is to say the reac- tion of the robot to the detection of the object.

Object oriented simulation of hybrid renewable energy systems focused on supervisor control

EFTA 2009

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