• Corpus ID: 14006552

Self-Organizing Maps In Natural Language Processing

@inproceedings{Honkela1997SelfOrganizingMI,
  title={Self-Organizing Maps In Natural Language Processing},
  author={Timo Honkela},
  year={1997}
}
Kohonen's Self-Organizing Map (SOM) is one of the most popular arti cial neural network algorithms. Word category maps are SOMs that have been organized according to word similarities, measured by the similarity of the short contexts of the words. Conceptually interrelated words tend to fall into the same or neighboring map nodes. Nodes may thus be viewed as word categories. Although no a priori information about classes is given, during the self-organizing process a model of the word classes… 
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106 Citations
Highly Influential Citations
8
Background Citations
26
Methods Citations
46
Results Citations
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106 Citations

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This chapter presents how the SOM model can be applied in Web mining, by giving sets of documents as input data space for SOM and how a more complex SOM based unsupervised neural network model is used for enriching a domain ontology.
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The clustering capability of the system allows users to find out quickly what is new in a Web site of interest by co mparing the clusters obtained from the site at different moment s in time.
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Uncovering Associations Between DocumentsDieter Merkl
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This paper presents the novel La-belSOM method, which automatically selects the most descriptive features of the input patterns mapped onto a particular unit of the map, thus making the associations between the various clusters within the map explicit.
SEMANTIC CLUSTERING OF VERBS Analysis of Morphosyntactic Contexts Using the SOM Algorithm
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  • 2009
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The self-organizing map algorithm is used to visualize and cluster common Finnish verbs based on functional and semantic information coded by case marking and function words like postpositions and adverbs to demonstrate the importance of cultural, social and emotional dimensions in lexical dimensions.
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Mining massive document collections by the WEBSOM method
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