Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Self-organizing maps
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
TextTiling: A Quantitative Approach to Discourse
TextTiling: A Quantitative Approach to Discourse
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Fast self-organizing feature map algorithm
IEEE Transactions on Neural Networks
Class distribution on SOM surfaces for feature extraction and object retrieval
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Adaptive topological tree structure for document organisation and visualisation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
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Exploration of text corpora using self-organizing maps has shown promising results in recent years. Topographic map approaches usually use the original vector space model known from Information Retrieval for text document representation. In this paper I present a two stage model using features based on sentence categories as alternative approach which includes contextual information. Algorithmic optimizations required by this computationally expensive model are shown and evaluated. Also a method for model independent comparison of document maps by evaluation of document distribution on maps is introduced and used to compare results obtained with both the new model and the vector space model.