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Contextualized Meaning Extraction: A Meta-Algorithm for Big Data Text Mining with Pragmatics

Contextualized Meaning Extraction: A Meta-Algorithm for Big Data Text Mining with Pragmatics

Jeffrey D. Wall, Rahul Singh
Copyright: © 2017 |Volume: 7 |Issue: 3 |Pages: 15
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781522513278|DOI: 10.4018/IJOCI.2017070102
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MLA

Wall, Jeffrey D., and Rahul Singh. "Contextualized Meaning Extraction: A Meta-Algorithm for Big Data Text Mining with Pragmatics." IJOCI vol.7, no.3 2017: pp.15-29. http://doi.org/10.4018/IJOCI.2017070102

APA

Wall, J. D. & Singh, R. (2017). Contextualized Meaning Extraction: A Meta-Algorithm for Big Data Text Mining with Pragmatics. International Journal of Organizational and Collective Intelligence (IJOCI), 7(3), 15-29. http://doi.org/10.4018/IJOCI.2017070102

Chicago

Wall, Jeffrey D., and Rahul Singh. "Contextualized Meaning Extraction: A Meta-Algorithm for Big Data Text Mining with Pragmatics," International Journal of Organizational and Collective Intelligence (IJOCI) 7, no.3: 15-29. http://doi.org/10.4018/IJOCI.2017070102

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Abstract

Text mining is a powerful form of business intelligence that is used increasingly to inform organizational decisions. Current text mining algorithms rely heavily on the lexical, syntactic, structural, and semantic features of text to extract meaning and insight for decision making. Although semantic analysis is a useful approach to meaning extraction, pragmatics suggests that a more accurate meaning of text can be extracted by examining the context in which the text is recorded. Given that massive amounts of textual data can be drawn from multiple and diverse sources, accounting for context is increasingly important. A conceptual model is provided to explain how concepts from pragmatics can improve existing text mining algorithms to provide more accurate information for decision making. Reversing the pragmatic process of meaning expression could lead to improved text mining algorithms. The theoretical process model developed herein can provide insight into the development and refinement of text mining algorithms that draw from diverse sources.

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