Abstracting for Dimensionality Reduction in Text Classification

• Published in: International journal of intelligent systems Vol. 28; no. 2; pp. 115 - 138
• Main Authors: McAllister, Richard A., Angryk, Rafal A.
• Format: Journal Article
• Language: English
• Published: Hoboken, NJ Blackwell Publishing Ltd 01.02.2013; Wiley; John Wiley & Sons, Inc
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Classification; Computer science; control theory; systems; Data mining; Data processing. List processing. Character string processing; Data structures; Exact sciences and technology; Information systems. Data bases; Intelligent systems; Memory organisation. Data processing; Organizations; Semantics; Software; Speech and sound recognition and synthesis. Linguistics; Studies; Tasks; Texts; Input output; Data analysis; Dimensionality; Keyword; Ontology; Information retrieval; Algebraic semantic; Text; Abstraction; Data mining; Modeling; Dimension reduction; Software tool; Linguistics; Natural language; Data models; Data structure; Content analysis
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1002/int.21543

There is a growing interest in efficient models of text mining and an emergent need for new data structures that address word relationships. Detailed knowledge about the taxonomic environment of keywords that are used in text documents can provide valuable insight into the nature of the subject matter contained therein. Such insight may be used to enhance the data structures used in the text data mining task as relationships become usefully apparent. A popular scalable technique used to infer these relationships, while reducing dimensionality, has been Latent Semantic Analysis. We present a new approach, which uses an ontology of lexical ions to create ion profiles of documents and uses these profiles to perform text organization based on a process that we call frequent ion analysis. We introduce TATOO, the Text ion TOOlkit, which is a full implementation of this new approach. We present our data model via an example of how taxonomically derived ions can be used to supplement semantic data structures for the text classification task.