Learning Document Similarity Using Natural Language Processing
AbstractThe recent considerable growth in the amount of easily available on-line text has brought to the foreground the need for large-scale natural language processing tools for text data mining. In this paper we address the problem of organizing documents into meaningful groups according to their content and to visualize a text collection, providing an overview of the range of documents and of their relationships, so that they can be browsed more easily. We use Self-Organizing Maps (SOMs) (Kohonen 1984). Great efficiency challenges arise in creating these maps. We study linguistically-motivated ways of reducing the representation of a document to increase efficiency and ways to disambiguate the words in the documents.
Merlo, P., Henderson, J., Schneider, G., & Wehrli, E. (2013). Learning Document Similarity Using Natural Language Processing. Linguistik Online, 17(5). https://doi.org/10.13092/lo.17.788
Copyright (c) 2013 Paola Merlo, James Henderson, Gerold Schneider, Eric Wehrli
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