Corpus ID: 14006552

Self-Organizing Maps In Natural Language Processing

@inproceedings{Honkela1997SelfOrganizingMI,
  title={Self-Organizing Maps In Natural Language Processing},
  author={T. 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… Expand
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110 Citations
Highly Influential Citations
7
Background Citations
25
Methods Citations
46
Results Citations
1

110 Citations

Citation Type

Citation Type

Self-organizing Maps in Web Mining and Semantic Web
TLDR
This chapter presents how the self-organizing map (SOM) model can be applied in Web mining, and how a more complex SOM based unsupervised neural network model is used for enriching a domain ontology. Expand
Self-organizing Maps in Web Mining and Semantic Web 357 Self-organizing Maps in Web Mining and Semantic Web
The nature inspired approaches represent a new trend in computer science in general and in the Semantic Web, due to their scalability and robustness. Neural networks represent one category of nature… Expand
Discovering Web Document Clusters with Self-Organizing Maps
TLDR
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. Expand
Analysis of functional similarities of Finnish verbs using the self-organizing map
TLDR
The results show that even the simple feature selection used in this experiment was found to be suitable for rough automatic categorization of verbs on the basis of data extracted from unrestricted texts and the automatically obtained organization resembles the semantic classification of verbs designed by a linguist. Expand
Uncovering Associations Between DocumentsDieter Merkl
The self-organizing map is a very popular un-supervised neural network model for the analysis of high-dimensional input data as it is typically found in information retrieval applications. However,… Expand
SEMANTIC CLUSTERING OF VERBS Analysis of Morphosyntactic Contexts Using the SOM Algorithm
Obtaining semantic or functional word categories from data in an unsupervised manner is a problem motivated both from the linguistic point of view and from that of construing language models for… Expand
Self-Organising Maps in Document Classification: A Comparison with Six Machine Learning Methods
TLDR
The self-organising map proved to be yielding the highest accuracies of tested unsupervised methods in classification of the Reuters news collection and the Spanish CLEF 2003 news collection, and comparable accuracies against some of the supervised methods in all three data sets. Expand
A study of the use of self-organising maps in information retrieval
Purpose – The aim of this paper is to explore the possibility of retrieving information with Kohonen self‐organising maps, which are known to be effective to group objects according to their… Expand
Corpus analysis and results visualisation using self-organizing maps
This paper addresses the related issues of statistical analysis of text corpora and of intuitively-accessible representation of the results of such analysis, with reference to the Newcastle-Poitiers… Expand
Mining massive document collections by the WEBSOM method
TLDR
This work contains an overview to the WEbsOM method and its performance, and as a special application, the WEBSOM map of the texts of Encyclopaedia Britannica is described. Expand
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References

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TLDR
This article presents a method, WEBSOM, for automatic organization of full-text document collections using the self-organizing map (SOM) algorithm, and presents a case study of its use. Expand
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  • T. Kohonen
  • Computer Science
  • Proceedings of International Conference on Neural Networks (ICNN'97)
  • 1997
TLDR
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TLDR
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TLDR
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TLDR
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TLDR
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