A neural network model for deriving meaning vectors for words from information retrieval based document vectors is described. A small scale experiment is performed using the model and results compared with an actual thesaurus. Although the results are not good, they show that a full scale experiment may be worth investigating to determine if the poor results are due to the model itself or the various flaws in the experiment. INTRODUCTION One important task necessary for performing natural language processing using a neural net is the assignment of appropriate representations for words or lexical items. Such representations are useful for such tasks as word sense disambiguation ("He hit the ball with a bat." vs. "He caught the bat with a net."). Localist representations are not desirable since both the differences in meaning between the same word in different contexts and the similarities in meaning between synonyms are not naturally represented. A distributed representation, or "meani...

this article I had in mind when trying to find a name for the model that I will describe in this chapter. Although at a different level, I too want to make explicit goals of connectionism, to state some fundamentals and to describe its relation to other participants within the cognitive field. The `level of entry' is different, however, since the model describes in a methodological way the manner in which connectionist progress goes and can therefore be taken to be a `meta'-view. Smolensky fills in important parts (i.e. the introduction of subsymbols) in the contents of the connectionist approach. I want to state some fundamental hypotheses about the nature of progress within this field and I will use the model to look at the differences between connectionism and symbolism. Together with the shape the model has taken, `Stagewise Treatment of Connectionism' (STC) seemed to be a logical choice. What will follow is a first introduction of these stages and the way they are connected. To give an indication of why these four stages are appropriate, quotes from several `leading figures' will be given to indicate on what basis the concept of these stages has been developed. In the next section, this first approximation of the model will be expanded to give a complete picture of the several ways in which artificial neural networks are currently applied to gain insights into the workings of brains. This will be followed by a simple example of the application of STC. So let's take a look at the way it works. Stage 1 Modelling on the basis of assumptions about the brain. The first of the four stages is the generation of network models based on some very basic assumptions about the brain, i.e. there are elements which are connected with each other through connections which can be mo...

Conceptual change can be accounted for at various levels of explanation. The cognitive level (Marr's computational level), the representational (Marr's "algorithmic"), and the implementational level. In this paper, we offer a dynamical account of types of conceptual change at the representational level. Our aim is to show that some classes of neural models can implement the types of change that we have proposed elsewhere. First we briefly describe at the cognitive level certain types of change that purport to account for some of the kinds of conceptual change. Then we lay forth the framework of dynamical connectionism; we discuss the representational level realizations of the cognitive level and claim that these can be depicted as points in the system's activational landscape. We offer, third, a dynamical account of some types change and we claim that conceptual change can be modeled as a process of modification, appearance of new and disappearance of attractors and/or basins of attraction that shape the system's landscape. Finally, we discuss the kinds of mechanisms at the representational level that could produce the types of change observed at the cognitive level, as modeled by means of dynamic connectionism.

the mapping from meaning to sounds

culture and the embodiment of spatial cognition