Joseph Busch / AI vs. Automation – The Current State of Automated Content Tagging

Apr. 1, 2018
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
Joseph Busch   /   AI vs. Automation – The Current State of Automated Content Tagging
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Joseph Busch / AI vs. Automation – The Current State of Automated Content Tagging

Editor's Notes

  1. Source: A star-forming region in the Large Magellanic Cloud (https://en.wikipedia.org/wiki/Star#/media/File:Starsinthesky.jpg)
  2. Source: A diagram of Constellations. (http://www.tate.org.uk/visit/tate-liverpool/display/constellations)
  3. Mel Bochner-Blah, Blah, Blah (2011)
  4. Mel Bochner-Blah, Blah, Blah (2011)
  5. Studies show that levels of indexer consistency vary, and that high levels of consistency are rare for: Indexing Choosing keywords Prioritizing index terms Choosing search terms Assessing relevance Choosing hypertext links Markey, K. "Inter-indexer consistency tests: A literature review and report of a test of consistency in indexing visual materials." Library and Information Science Research, 6, 155-177, 1984. Semantic tools and automated processes can help guide users to be more consistent.
  6. Source: https://thumb9.shutterstock.com/display_pic_with_logo/1735900/147138632/stock-photo-robot-handshake-147138632.jpg
  7. What’s changed?
  8. IBM Watson has popularized the use of automatic tagging to solve problems. What is it? A cloud service so you no longer have to stand up an application server and complex software before you can even get started. Of course there is still real work to do to configure the application to work with your stuff.
  9. Source: http://www.thinkstockphotos.com/image/stock-photo-internet-of-things-iot-word-on-wood-floor-with/488596460 Generating data from everything, everywhere. This is especially useful with expensive equipment and machinery like medical diagnostic equipment, electric generator turbines or jet engines that can be monitored to identify anomalies before system failure.
  10. Processing text to determine whether a phrase or document is positive, negative or neutral. Lexalytics Development Blog: Adjusting out-of-the-box Salience sentiment. http://dev.lexalytics.com/blog/?p=19
  11. Processing text to automatically identify key terms or phrases (keywords) that represent the most relevant information contained in the document. http://fivefilters.org/term-extraction/
  12. Processing text to automatically identify key sentences that represent the most relevant information contained in the document.
  13. Boolean search is a type of search allowing users to combine keywords with operators (or modifiers) such as AND, NOT and OR to further produce more relevant results. Saving a Boolean search is a common method used to scope topics in automated classification tools.
  14. Source: http://pestleanalysis.com/wp-content/uploads/2011/12/examples-of-pestle-analysis.jpg Machine learning techniques can be applied when a collection of items has already been categorized. A collection of pre-categorized items is known as the training set. Using the training set, the categorizer “learns” the words that occur, and co-occur, in each category. Although it is called learning or training, what is really happening is that the algorithm is building lists of words and computing statistics about them. Once the algorithm has built up information on the relative frequencies of various words in the different categories, new documents have their word information compared to the data stored for each category. The document is then tagged with the category that is best matched.
  15. The Vector Space Model was first used by the SMART (System for the Mechanical Analysis and Retrieval of Text) Retrieval System developed by Gerald Salton at Cornell University in the 1960s. In this model, each document is represented by a term vector (i.e., a column of numbers generated by analyzing the document’s content). When a new document needs to be categorized, one compares its vector to all the others and sees which one or ones it is closest to. Bayesian Classifiers, Support Vector Machines, Hidden Markov Models, and other advanced categorization techniques are all based on this foundation.
  16. The underlying classification methods are often obscured by packaged software companies. Concept Searching (https://www.conceptsearching.com/) Data Harmony Machine Aided Indexer (M.A.I.) - (http://www.accessinn.com/products/maistro/mai/) Expert System Cogito Studio (http://www.expertsystem.com/products/cogito-studio/) Mondeca Content Annotation Manager (http://www.mondeca.com/content-annotation-manager/) PoolParty Text Mining & Entity Extraction (https://www.poolparty.biz/text-mining-entity-extraction/) SmartLogic Classification Server (https://www.smartlogic.com/products/classification-server)