MGT-4030 Study Guide - Final Guide: Natural-Language Processing, Text Mining, Unstructured Data
20 Aug 2015
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Document Summary
Neural computing: very good at capturing highly complex non-linear functions, uses. Service operation, information systems, etc: data mining vs. Nature of the data: structured data- databases, unstructured data- word documents, pdf files, text excerpts, xml files, and so on. Text mining- first, impose structure to the data, then mine the structured data: text mining- a semi-automated process of extracting knowledge from unstructured data sources, benefits of text mining in text rich data environments. Medicine- discharge summaries: electronic communication records. Automatic response generation: natural language processing (nlp, structuring a collection of text. Text, xml, html: task 1- establish the corpus- collect and organize the domain specific unstructured data. Output- a collection of documents in some digitized format for computer processing: task 2- create the term-document matrix- introduce structure to the corpus. Output- a flat file called term-document matrix where the cells are populated with the term frequencies: task 3- extract knowledge- discover novel patterns from the t-d matrix.
