Abstract
Associative networks are a connectionist language model with the ability to handle large sets of documents. In this research we investigated the use of natural language processing techniques (part-of-speech tagging and parsing) in combination with Associative Networks for document categorization and compare the results to a TF-IDF baseline. By filtering out unwanted observations and preselecting relevant data based on sentence structure, natural language processing can pre-filter information before it enters the associative network, thus improving results.
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Bloom, N. (2012). Using Natural Language Processing to Improve Document Categorization with Associative Networks. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds) Natural Language Processing and Information Systems. NLDB 2012. Lecture Notes in Computer Science, vol 7337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31178-9_18
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DOI: https://doi.org/10.1007/978-3-642-31178-9_18
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