Comparisons of Self-organized Word Category Maps
نویسنده
چکیده
It has been shown earlier that the Self-Organizing Map (SOM) can be applied to the analysis and visualization of similarities of words in their usage in short contexts formed of adjacent words. These SOMs of words, called word category maps, have many potential applications. One of the application areas is information retrieval and data mining of textual document collections where the word category maps can be used in document encoding as has been demonstrated in the WEBSOM project. This paper concentrates on the question of how to create good word category maps, and speciically, how to compare diierent map instances. A general map comparison method that takes into account both the map topology and nonlinearity of the SOM is used. The paper presents results of comparisons in two experiments: rst related to the number of words on a map, and second concerning the neighborhood type used in the SOM algorithm.
منابع مشابه
Self-organizing Maps in Natural Language Processing
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 prior...
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