Class (philosophy)

Abstract: Categorizations which humans make of the concrete world are not arbitrary but highly determined. In taxonomies of concrete objects, there is one level of abstraction at which the most basic category cuts are made. Basic categories are those which carry the most information, possess the highest category cue validity, and are, thus, the most differentiated from one another. The four experiments of Part I define basic objects by demonstrating that in taxonomies of common concrete nouns in English based on class inclusion, basic objects are the most inclusive categories whose members: (a) possess significant numbers of attributes in common, (b) have motor programs which are similar to one another, (c) have similar shapes, and (d) can be identified from averaged shapes of members of the class. The eight experiments of Part II explore implications of the structure of categories. Basic objects are shown to be the most inclusive categories for which a concrete image of the category as a whole can be formed, to be the first categorizations made during perception of the environment, to be the earliest categories sorted and earliest named by children, and to be the categories most codable, most coded, and most necessary in language.

Abstract: We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "four stars" than to "one star".We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.

Abstract: A general logic program (abbreviated to "program" hereafter) is a set of roles that have both positive and negative subgoals. It is common to view a deductive database as a general logic program consisting of rules (IDB) slttmg above elementary relations (EDB, facts). It is desirable to associate one Herbrand model with a program and think of that model as the "meaning of the program, " or Its "declarative semantics. " Ideally, queries directed to the program would be answered in accordance with this model. Recent research indicates that some programs do not have a "satisfactory" total model; for such programs, the question of an appropriate partial model arises. Unfounded sets and well-founded partial models are introduced and the well-founded semantics of a program are defined to be its well-founded partial model. If the well-founded partial model is m fact a total model. it is called the well-founded model. It n shown that the class of programs possessing a total well-founded model properly includes previously studied classes of "stratified" and "locally stratified" programs, The method in this paper is also compared with other proposals in the literature, including Clark's "program completion, " Fitting's and Kunen's 3-vahred interpretations of it, and the "stable models" of Gelfond and Lifschitz.

Abstract: An introduction is given to a theory of early visual information processing. The theory has been implemented, and examples are given of images at various stages of analysis. It is argued that the first step of consequence is to compute a primitive but rich description of the grey-level changes present in an image. The description is expressed in a vocabulary of kinds of intensity change (EDGE, SHADING-EDGE, EXTENDED-EDGE, LINE, BLOB etc.). Modifying parameters are bound to the elements in the description, specifying their POSITION, ORIENTATION, TERMINATION points, CONTRAST, SIZE and FUZZINESS. This description is obtained from the intensity array by fixed techniques, and it is called the primal sketch. For most images, the primal sketch is large and unwieldy. The second important step in visual information processing is to group its contents in a way that is appropriate for later recognition. From our ability to interpret drawings with little semantic content, one may infer the presence in our perceptual equipment of symbolic processes that can define "place-tokens" in an image in various ways, and can group them according to certain rules. Homomorphic techniques fail to account for many of these grouping phenomena, whose explanations require mechanisms of construction rather than mechanisms of detection. The necessary grouping of elements in the primal sketch may be achieved by a mechanism that has available the processes inferred from above, together with the ability to select items by first order discriminations acting on the elements' parameters. Only occasionally do these mechanisms use downward-flowing information about the contents of the particular image being processed. It is argued that "non-attentive" vision is in practice implemented by these grouping operations and first order discriminations acting on the primal sketch. The class of computations so obtained differs slightly from the class of second order operations on the intensity array. The extraction of a form from the primal sketch using these techniques amounts to the separation of figure from ground. It is concluded that most of the separation can be carried out by using techniques that do not depend upon the particular image in question. Therefore, figure-ground separation can normally precede the description of the shape of the extracted form. Up to this point, higher-level knowledge and purpose are brought to bear on only a few of the decisions taken during the processing. This relegates the widespread use of downward-flowing information to a later stage than is found in current machine-vision programs, and implies that such knowledge should influence the control of, rather than interfering with, the actual data-processing that is taking place lower down.

Abstract: Types of A'-Dependencies develops the theories of Bonding and Government of the "principles and parameters" approach to syntax pioneered by Noam Chomsky. Using data from Romance languages, Cinque argues for a particular way of delimiting the descriptive generalizations that concern the grammar of constituent extraction, and the principles from which they derive.Cinque starts by distinguishing four major cases of A'-Dependencies on the basis of their different behavior with respect to island conditions. He discusses the distinction between "long" and "successive cyclic" wh-movement, indicating restrictions on the class of elements able to undergo "long" wh-movement and offering a simplification of the locality conditions on the two types of movement.Cinque then introduces a Romance construction, Clitic Left Dislocation, to show the value of separating the two types of wh-movement and offers a theory that explains certain differences between NPs and non-NPs under extraction.Guglielmo Cinque is a Professor on the Faculty of Linguistics at the University of Venice.