Histology is the science of understanding the structure of animals and plants, and studying the functional implications of biological structures. In this paper, we propose a statistical modeling approach to human histological image categorization. Texture features of the images are characterized by localized Gabor filters. The probabilistic distribution of the texture patterns from each category is approximated by a finite Gaussian mixture model. Expectation maximization (EM) procedure and minimum message length (MML) principle are used to perform density estimation and model selection, respectively. Component-wise EM and weak component annihilation are applied to avoid the drawbacks of the standard EM. Experimental validation is provided based on images from different organs and parts of the body.

Intelligibility and fidelity are the two key notions in machine translation system evaluation, but do not always provide enough information for system development. Detailed information about the type and number of errors of each type that a translation system makes is important for diagnosing the system, evaluating the translation approach, and allocating development resources. In this paper, we present a fine-grained machine translation evaluation framework that, in addition to the notions of intelligibility and fidelity, includes a typology of errors common in automatic translation, as well as several other properties of source and translated texts. The proposed framework is informative, sensitive, and relatively inexpensive to apply, to diagnose and quantify the types and likely sources of translation error. The proposed fine-grained framework has been used in two evaluation experiments on the LMT English-Spanish machine translation system, and has already suggested one important architectural improvement of the system.

The syntactic analysis of languages with respect to Government-binding (GB) grammar is a problem that has received relatively little attention until recently. This paper describes an attribute grammar specification of the Government-binding theory. The paper focuses on the description of the attribution rules responsible for determining antecedent-trace relations in phrase-structure trees, and on some theoretical implications of those rules for the GB model. The specification relies on a transformation-less variant of Government-binding theory, briefly discussed by Chomsky (1981), in which the rule move-α is replaced by an interpretive rule. Here the interpretive rule is specified by means of attribution rules. The attribute grammar is currently being used to write an English parser which embodies the principles of GB theory. The parsing strategy and attribute evaluation scheme are cursorily described at the end of the paper.