This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed

is simulated without pronunciation rules, and lexical decisions are simulated without accessing word-level representations. The performance of the model is largely determined by three factors: the nature of the input, a significant fragment of written English; the learning rule, which encodes the implicit

We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic

describe a method for statistical modeling based on maximum entropy. We present a maximum-likelihood approach for automatically constructing maximum entropy models and describe how to implement this approach efficiently, using as examples several problems in natural language processing.

this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learning method applied to part of speech tagging

God, having designed man for a sociable creature, made him not only with an inclination and under a necessity to have fellowship with those of his own kind, but furnished him also with language, which was to be the great instrument and common tie of society.

This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur only after practice in a consistent environment. Practice is important because it increases the amount retrieved and the speed of retrieval; consistency is important because it ensures that the retrieved instances will be useful. The theory accounts quantitatively for the power-function speed-up and predicts a power-function reduction in the standard deviation that is constrained to have the same exponent as the power function for the speed-up. The theory accounts for qualitative properties as well, explaining how some may disappear and others appear with practice. More generally, it provides an alternative to the modal view of automaticity, arguing that novice performance is limited by a lack of knowledge rather than a scarcity of resources. The focus on learning avoids many problems with the modal view that stem from its focus on resource limitations. Automaticity is an important phenomenon in everyday men-tal life. Most of us recognize that we perform routine activities quickly and effortlessly, with little thought and conscious aware-ness--in short, automatically (James, 1890). As a result, we of-ten perform those activities on "automatic pilot " and turn our minds to other things. For example, we can drive to dinner while conversing in depth with a visiting scholar, or we can make coffee while planning dessert. However, these benefits may be offset by costs. The automatic pilot can lead us astray, caus-ing errors and sometimes catastrophes (Reason & Myceilska, 1982). If the conversation is deep enough, we may find ourselves and the scholar arriving at the office rather than the restaurant, or we may discover that we aren't sure whether we put two or three scoops of coffee into the pot. Automaticity is also an important phenomenon in skill acqui-sition (e.g., Bryan & Harter, 1899). Skills are thought to consist largely of collections of automatic processes and procedures

The literature on interference in the Stroop Color-Word Task, covering over 50 years and some 400 studies, is organized and reviewed. In so doing, a set ofl 8 reliable empirical findings is isolated that must be captured by any successful theory of the Stroop effect. Existing theoretical positions are summarized and evaluated in view of this critical evidence and the 2 major candidate theories--relative speed of processing and automaticity of reading--are found to be wanting. It is concluded that recent theories placing the explanatory weight on parallel processing of the irrelevant and the relevant dimensions are likely to be more successful than are earlier theories attempting to locate a single bottleneck in attention. In 1935, J. R. Stroop published his landmark article on attention and interference, an article more influential now than it was then. Why has the Stroop task continued to fascinate us? Perhaps the task is seen as tapping into the primitive operations of cognition, offering clues to the fundamental process of attention. Perhaps the robustness of the phenomenon provides a special challenge to decipher. Together these are powerful attractions

WordNet is an on-line lexical reference system whose design is inspired by current

This article presents a measure of semantic similarityinanis-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their e#ectiveness. 1. Introduction Evaluating semantic relatedness using network representations is a problem with a long history in arti#cial intelligence and psychology, dating back to the spreading activation approach of Quillian #1968# and Collins and Loftus #1975#. Semantic similarity represents a special case of semantic relatedness: for example, cars and gasoline would seem to be more closely related than, say, cars and bicycles, but the latter pair are certainly more similar. Rada et al. #Rada, Mili, Bicknell, & Blett...