WordNet: a lexical database for English
Communications of the ACM
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Supervised Latent Semantic Indexing for Document Categorization
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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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.