We describe a method for automatic word sense disambiguation using a text corpus and a machinereadable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method can learn even from very sparse training data, achieving over 92 % correct disambiguation performance.

ABSTRACT. This paper discusses the general problem of translation functions between logics, given in axiomatic form, and in particular, the problem of determining when two such logics are “synonymous ” or “translationally equivalent. ” We discuss a proposed formal definition of translational equivalence, show why it is reasonable, and also discuss its relation to earlier definitions in the literature. We also give a simple criterion for showing that two modal logics are not translationally equivalent, and apply this to well-known examples. Some philosophical morals are drawn concerning the possibility of having two logical systems that are “empirically distinct ” but are both translationally equivalent to a common logic. KEY WORDS: modal logic, synonymy, translation 1.

This article reviews research on the use of situation models in lnguage comprehension and memory retrieval over the past 15 years. Situation models are integrated mental representations of a described state of affairs. Significant progress has been made in the scientific understanding of how situation models are involved in language comprehension and memory retrieval. Much of this research focuses on establishing the existence of situation models, often by using tasks that assess one dimension of a situation model. However, the authors argue that the time has now come for researchers to begin to take the multidimensionality of situation models seriously. The authors offer a theoretical framework and some methodological observations that may help researchers to tackle this issue.

The traditional notion of word meaning used in natural language processing is literal or lexical meaning as used in dictionaries and lexicons. This relatively objective notion of lexical meaning is different from more subjective notions of emotive or affective meaning. Our aim is to come to grips with subjective aspects of meaning expressed in written texts, such as the attitude or value expressed in them. This paper explores how the structure of the WordNet lexical database might be used to assess affective or emotive meaning. In particular, we construct measures based on Osgood’s semantic differential technique. Suppose we can evaluate the subjective meaning expressed in a text. This would allow us to classify documents on subjective criteria, rather than on their factual content. There are several potential applications for such classifications, for example, providing summary statistics for search engines. Given the query “Crete travel review”, a search engine could report, “There are 1000 hits of which 3/4 is a positive review”. Another potential application is filtering “flames ” for newsgroups.

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It is proposed to conceive of representation as an emergent phenomenon that is supervenient on patterns of activity of coarsely tuned and highly redundant feature detectors. The computational underpinnings of the outlined theory of representation are (1) the properties of collections of overlapping graded receptive fields, as in the biological perceptual systems that exhibit hyperacuity-level performance, and (2) the sufficiency of a set of proximal distances between stimulus representations for the recovery of the corresponding distal contrasts between stimuli, as in multidimensional scaling. The present preliminary study appears to indicate that this concept of representation is computationally viable, and is compatible with psychological and neurobiological data. 1 Introduction A perceptual system confronted with a stimulus must (i) decide whether the stimulus belongs to an already encountered category, and (ii) if necessary, create a new category record for the stimulus a...

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply `pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively.

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. We report on a case study in the emergence of a lexicon in a group of autonomous distributed agents situated and grounded in an open environment. Because the agents are autonomous, grounded, and situated, the possible words and possible meanings are not fixed but continuously change as the agents autonomously evolve their communication system and adapt it to novel situations. The case study shows that a complex semiotic dynamics unfolds and that generalisations present in the language are due to processes outside the agent. 1 Introduction In recent years it has become clear that the complex adaptive systems approach pioneered by Artificial Life research can fruitfully be applied to the study of the origins and evolution of language [9], particularly to the emergence of shared sound systems [3], the self-organisation of lexicons [7], [11], grounded word meaning [12], and the origins of grammar [4], [1], [5]. In all this research, the same mechanisms for the generation and mai...

Over the first year of life, infants gain conceptual skills which allow them to construe semantically related items as similar, even when they have few if any directly-perceived attributes in common. Moreover, this skill first encompasses only broad semantic categories, and only later extends to more subtle distinctions, when conceptual and perceptual similarity relations do not coincide. In this paper we suggest that a new mechanism must be added to the mix of possible bases for this observed developmental change. In agreement with many others, we suggest that infants’ earliest conceptual representations are organised with respect to certain especially useful or salient properties, regardless of whether such properties can be directly observed. However we suggest that in many cases this salience may itself be acquired, through domain-general learning mechanisms that are sensitive to the high-order coherent covariation of directly-observed stimulus properties across a breadth of experience. To support this argument we will describe simulations with a simple PDP model of semantic memory. When trained with backpropagation to complete queries about the properties of different objects, the model’s internal representations differentiate in a coarse-to-fine manner. As a consequence, different sets of properties come to be especially “salient” to the