Textual Data Analytics Task

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A Textual Data Analytics Task is a data analytics task that is a text processing task (whose task input is a text dataset).




  • (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/text_mining#Text_analysis_processes Retrieved:2015-4-1.
    • Subtasks — components of a larger text-analytics effort — typically include:
      • Information retrieval or identification of a corpus is a preparatory step: collecting or identifying a set of textual materials, on the Web or held in a file system, database, or content management system, for analysis.






  • (Bilisoly, 2008) ⇒ Roger Bilisoly. (2008). “Practical Text Mining with Perl.” Wiley Series on Methods and Applications in Data Mining




  • (Chen et al., 2005) ⇒ Hsinchun Chen, Sherrilynne S. Fuller, and William Hersh. (2005). “Medical Informatics: knowledge management and data mining in biomedicine." Springer. ISBN:038724381X,
    • QUOTE:Text mining aims to extract useful knowledge from textual data or documents (Hearst, 1999; Chen, 2001). Although text mining is often considered a subfield of data mining, some text mining techniques have originated from other disciplines, such as information retrieval, information visualization, computational linguistics, and information science. Examples of text mining applications include document classification, document clustering, entity extraction, information extraction, and summarization.

      Most knowledge management, data mining, and text mining techniques involve learning patterns from existing data or information, and are therefore built upon the foundations of machine learning and artificial intelligence. In the following, we review several major paradigms in machine learning, important evaluation methodologies, and their applicability in biomedicine.