Text analytics consist of the statistics about a text element, which includes the word count, the word histogram, and the word frequency histogram. Most text documents of value are related to other-sometimes many other-documents, and so analytics describing the relative frequency of terms in a document compared to its peers are important for defining key words (tagging, labeling, indexing), search-responsive terms (query terms), and compressed versions of the documents (key words, summary, etc.).This clearly written text explains the functional applications of search, translation, optimization, and learning with regard to text analytics. Generation of analytics is aided by a hybrid, ensemble, or other combinatorial approach in which two or more effective analytic processes are used simultaneously, and their outputs combined to form a better "consensus". Additional value to the preservation of the information is provided through these methods. Also, since they encompass capabilities of two or more knowledge-generating systems, they can create a "superset" of access points to the data generated. The book also describes the role of functional approaches in the testing and configuration of these systems.
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Steve Simske is a Professor in the Systems Engineering Department at Colorado State University (CSU), where he leads research on analytics, cybersecurity, sensing, imaging, and robotics. A former Fellow in HP Labs, Steve was in the computer and printing industries for 23 years before joining CSU in 2018. He holds more than 200 US Patents and has more than 450 publications. This is his fourth book.
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