ACM SIGIR Forum
Self-organizing maps
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Very Large Two-Level SOM for the Browsing of Newsgroups
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Two-level Clustering of Web Sites Using Self-Organizing Maps
Neural Processing Letters
Using ontology-based approaches to representing speech transcripts for automated speech scoring
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
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WEBSOM is a recently developed neural method for exploring full-textdocument collections, for information retrieval, and for informationfiltering. In WEBSOM the full-text documents are encoded as vectorsin a document space somewhat like in earlier information retrievalmethods, but in WEBSOM the document space is formed in anunsupervised manner using the Self-Organizing Map algorithm. In thisarticle the document representations the WEBSOM creates are shown tobe computationally efficient approximations of the results of acertain probabilistic model. The probabilistic model incorporatesinformation about the similarity of use of different words to takeinto account their semantic relations.