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The Self-Organizing Map (SOM) is an artificial neural network model based on unsupervised learning. In this paper, the use of the SOM in natural language processing is considered. The main emphasis is on natural features of natural language including contextuality of interpretation, and the communicative and social aspects of natural language learning and usage. The SOM is introduced as a general method for the analysis and visualization of complex, multidimensional input data. The approach of how to process natural language input is presented. Some epistemological underpinnings are outlined, including the creation of emergent and implicit categories by SOM, intersubjectivity and relativity of interpretation, and the relation between discrete symbols and continuous variables. Finally, the use of SOM as a component in an anticipatory system is presented, and the relation between anticipation and self-organization is discussed.

Learning to understand—General aspects of using Self-Organizing Maps in natural language processing