Self-Organizing Maps in Natural Language Processing

摘要

Kohonen's Self-Organizing Map (SOM) is one of the most popular artificial neuralnetwork 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. The central topic of the thesis is the use of the SOM in natural language processing. The approach based on the word category maps is compared with the methods that are widely used in artificial intelligence research. Modeling gradience, conceptual change, and subjectivity of natural language interpretation are considered. The main application area is information retrieval and textual data mining for which a specific SOM-based method called the WEBSOM method organizes a document collection on a map considered. The main application area is information retrieval and textural data mining for which a specific SOM-based method called the WEBSOM has been developed. The WEBSOM method organizes a document collection on a map display that provides an overview of the collection and facilitates interactive browsing.