Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term–document, word–context, and pair–pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field. 1.

This paper describes a theory that explains both the creativity and the efficiency of people's conceptual combination. In the constraint theory, conceptual combination is controlled by three constraints of diagnosticity, plausibility, and informativeness. The constraints derive from the pragmatics of communication as applied to compound phrases. The creativity of combination arises because the constraints can be satisfied in many different ways. The constraint theory yields an algorithmic model of the efficiency of combination. The C model admits the full creativity of combination and yet efficiently settles on the best interpretation for a given phrase. The constraint theory explains many empirical regularities in conceptual combination, and makes various empirically verified predictions. In computer simulations of compound phrase interpretation, the C model has produced results in general agreement with people's responses to the same phrases

People often interpret novel noun-noun combinations by transferring a property from one constituent concept of the combination to the other. Two theories make different predictions about these `property' interpretations. Dual-process theory predicts that properties transferred will be alignable differences of the concepts being combined. Constraint theory predicts that properties transferred will be diagnostic properties of the concepts they originate in. An experimental study tested these contrasting predictions in interpretation comprehension and interpretation production tasks. The results showed that participants reliably preferred diagnostic property interpretations, whether alignable or non-alignable, in both tasks. There was no reliable preference for alignable interpretations in either task. This confirms constraint theory's predictions about property interpretations, and goes against the predictions of dual-process theory.

1.1. Conceptual systems 621 1.2. Semantic memory 621

Previous research has shown that background knowledge affects the ease of concept learning, but little research has examined its effects on speeded categorization of instances after the category is well learned. Subjects in 4 experiments first learned novel categories. At test, they categorized a new set of novel stimuli that were either consistent or inconsistent with background knowledge given about the categories. Background knowledge affected catego-rization responses in an untimed task, with usual reaction time instructions, with a response deadline, or when the stimuli were presented for 50 ms followed by a mask. Three other experiments using a part-detection task showed that subjects were more likely to notice missing parts that were critical than noncritical according to background knowledge. The mechanisms by which background knowledge affects categorization and part detection are discussed. Human categorization is a cognitive proceSs in which people decide whether an instance is a member of a cate-gory by comparing the instance with their conceptual rep-resentations. Categorization research in the 1970s and early

Most novel noun-noun combinations are polysemous in that they tend to suggest several possible meanings. A finger cup can be a cup in which fingers are washed, a cup shaped like a finger, a narrow cup and so on. In this paper, we present a new theory of concept combination, the constraint theory, that accounts for the polysemy of noun-noun combinations. Constraint theory, which uses three constraints (diagnosticity, plausibility and informativeness) acting over a unitary mechanism that generates candidate interpretations, makes certain predictions about the polysemy of different combinations. In particular, it predicts that combinations involving artifact terms should be more polysemous than those involving natural kinds because the former have functional models that promote multiple interpretations. In a single experiment, this prediction is confirmed along with other predictions about the types of interpretation that tend to be produced. Introduction Research on concept combination ...

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In cognitive psychology, concepts are those bodies of knowledge that are stored in long-term memory and are used by default in human beings ’ higher cognitive processes (categorization, inductive and deductive reasoning, etc.). Most psychologists of concepts assume that these mental representations share many scientifically important properties, and the psychology of concepts is expected to describe those properties. Psychologists assume thereby that concepts constitute a natural kind. I call this assumption the natural kind assumption. This article challenges the natural kind assumption. It is argued that a growing body of evidence suggests that concepts do not constitute a natural kind. Hence, the notion of concept is inappropriate, if one aims at formulating scientifically relevant inductive generalizations about the human mind. 1. Introduction. In cognitive science

Investigations of patients with semantic category-specific deficits have revealed a wide range of performance and variability in categories that are impaired or spared; this variability presents a challenge to accounts of category specificity. Accounts based only on impairment to semantic features of a particular type (e.g., visual), as well as accounts based only on featural properties (e.g., feature intercorrelations), are insufficient to explain the variability of patients' performance. A first goal of the paper is to discuss how a hybrid account incorporating both a level of organization according to feature-types (a level of nonlinguistic conceptual representations) and a level of organization dictated by featural properties may provide a more comprehensive account of the cases reported in the literature. The second and most novel goal of the study reported here is to derive from our hybrid account a series of novel predictions concerning the representation and impairment of a different domain of knowledge: knowledge of actions and events, a domain of knowledge that has received remarkably little attention to date. Keywords: category-specificity, nouns, verbs, semantics, simulation The breakdown of semantic knowledge: Insights from a statistical model of meaning representation. The study of patients in whom semantic knowledge has been disrupted has led to a number of important inferences concerning the underlying architecture of the semantic system (Warrington, 1975). Particularly relevant are cases in which focal brain damage creates categoryspecific deficits (i.e., selective impairment of semantic knowledge along category boundaries). At present there are a substantial number of cases on record (approximately 89, according to Rogers & Plaut, 2002). Specificity in...

It has been claimedthat concepts in differentsemantic domains vary in the extent to which their meaning is comprised of different kinds of semantic information. Discussion has mainly focused around two kinds of concepts—living things and man-made objects—arguing that functional information is central to the meaning of artefacts whereas perceptual information is more important for the meaning of living things. This distinction has been important in accounting for patterns of semantic impairments following brain injury (Warrington & Shallice, 1984). We suggest that functional information may be especially salient in the semantic representations of both living and nonliving things. Our evidence for this claim comes from priming studies with normal subjects, and data from brain-damaged patients that supports the claim that functional information is relatively spared following brain damage. We explore further implications of the role of functional properties in semantic representations,considering distinctions between different types of functional information in the representationof living things. We focus on the developmental claim that biological functional information,