Enrich machine learning with text classification
Machine learning is becoming more and more advanced, but it can be improved when applied within a framework of solid classification and analysis methods such as taxonomy, providing much-needed context.
In his latest whitepaper, Contextual Machine Learning: It’s Classified, Seth Grimes discusses current analytics models and shows how they can be supported and enriched by better text classification. He also outlines the five ways that machine learning accuracy can be improved by deep text classification.
Download the paper to learn how to:
Check outputs with a higher degree of accuracy than human evaluators
Apply eContext's deep linguistic library to automate your annotations
Make better predictions with taxonomy and machine learning
About the Author
Seth Grimes consults on business applications of natural language processing, text analytics, sentiment analysis, and data visualization. He founded Alta Plana Corporation in 1997 and the Sentiment Analysis Symposium conference series in 2010. He co-organizes the LT Accelerate conference in Brussels, a European forum for language-technology adoption. Follow him on Twitter at @SethGrimes.
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