Introduction to Text Miningand Semantics<br />Seth Grimes<br />--<br />President, Alta Plana<br />
New York Times<br />October 9, 1958<br />
From text to information<br />“Text expresses a vast, rich range of information, but encodes this information in a form th...
Document input and processing<br />Information extraction<br />Knowledge management<br />Hans Peter Luhn, “A Business Inte...
Statistical analysis of content<br />“Statistical information derived from word frequency and distribution is used by the ...
Statistical analysis limitations<br />“This rather unsophisticated argument on ‘significance’ avoids such linguistic impli...
Semantic links<br />New York Times,<br />September 8, 1957<br />Anaphora / coreference: “They”<br />
Digital content universe<br />“The broadcast, media, and entertainment industries garner about 4% of the world’s revenues ...
The “unstructured data” challenge<br />The digital universe:<br /><ul><li>Web sites, news & journal articles, images, video.
Blogs, forum postings, and social media.
E-mail, Contact-center notes and transcripts; recorded conversation.
Surveys, feedback forms, warranty & insurance claims.
Office documents, regulatory filings, reports, scientific papers.
And every other sort of document imaginable.</li></ul>Is Search up to the job?<br />
Search results<br />How are the quality, value & authority of search results?<br />Hotel’s opinion<br />Who profits from s...
From Web 1.0 to Web 2.0<br />How can we do better?<br />“We have many of the tools in place -- from Web 2.0 technologies…”...
Web 2.0<br />“Web 2.0 is the business revolution in the computer industry caused by the move to the Internet as a platform...
From information growth to economicgrowth<br />“We have many of the tools in place -- from Web 2.0 technologies… to unstru...
Text mining: from information to intelligence<br />Text mining enables smarter search that better responds to user goals, ...
From Web 2.0 to Web 3.0<br />For even better findability:<br />“The Semantic Web is a web of data, in some ways like a glo...
Linked Data.
Context sensitive.
Location aware.</li></li></ul><li>The Semantic Web vision<br />&quot;<br />An open-standards architure, coordinated by the...
Steps in the right direction…<br />
… and missteps<br />“Kind” = type, variety, not a sentiment.<br />Complete misclassification<br />External reference<br />...
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Introduction to Text Mining and Semantics

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Introduction to Text Mining and Semantics, presented by Seth Grimes at Nstein seminars in London, June 2009, and New York, September 2009.

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Introduction to Text Mining and Semantics

  1. 1. Introduction to Text Miningand Semantics<br />Seth Grimes<br />--<br />President, Alta Plana<br />
  2. 2. New York Times<br />October 9, 1958<br />
  3. 3. From text to information<br />“Text expresses a vast, rich range of information, but encodes this information in a form that is difficult to decipher automatically.” <br />-- Marti A. Hearst,<br />“Untangling Text Data Mining,” 1999<br />
  4. 4. Document input and processing<br />Information extraction<br />Knowledge management<br />Hans Peter Luhn, “A Business Intelligence System,” IBM Journal, October 1958<br />
  5. 5. Statistical analysis of content<br />“Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance.”<br />Hans Peter Luhn, <br />“The Automatic Creation of Literature Abstracts,” <br />IBM Journal, April 1958<br />
  6. 6. Statistical analysis limitations<br />“This rather unsophisticated argument on ‘significance’ avoids such linguistic implications as grammar and syntax... No attention is paid to the logical and semantic relationships the author has established.”<br />-- Hans Peter Luhn, 1958<br />
  7. 7. Semantic links<br />New York Times,<br />September 8, 1957<br />Anaphora / coreference: “They”<br />
  8. 8. Digital content universe<br />“The broadcast, media, and entertainment industries garner about 4% of the world’s revenues but already generate, manage, or otherwise oversee 50% of the digital universe.”<br />“The Diverse and Exploding Digital Universe,”<br />(IDC, 2008)<br />Approximately 70% of the digital universe is created by individuals.<br />
  9. 9. The “unstructured data” challenge<br />The digital universe:<br /><ul><li>Web sites, news & journal articles, images, video.
  10. 10. Blogs, forum postings, and social media.
  11. 11. E-mail, Contact-center notes and transcripts; recorded conversation.
  12. 12. Surveys, feedback forms, warranty & insurance claims.
  13. 13. Office documents, regulatory filings, reports, scientific papers.
  14. 14. And every other sort of document imaginable.</li></ul>Is Search up to the job?<br />
  15. 15. Search results<br />How are the quality, value & authority of search results?<br />Hotel’s opinion<br />Who profits from search?<br />Guest’s opinion - about Priceline<br />
  16. 16. From Web 1.0 to Web 2.0<br />How can we do better?<br />“We have many of the tools in place -- from Web 2.0 technologies…”<br />“The Diverse and Exploding Digital Universe,”<br />(IDC, 2008)<br />
  17. 17. Web 2.0<br />“Web 2.0 is the business revolution in the computer industry caused by the move to the Internet as a platform.” -- Tim O’Reilly, 2004<br />“[A] move from personal websites to blogs and blog site aggregation, from publishing to participation,… an ongoing and interactive process... to links based on tagging.”<br /> -- Terry Flew, “New Media: An Introduction,” 2008<br />Web 2.0 is dynamic, personalized, interactive, collaborative.<br />
  18. 18. From information growth to economicgrowth<br />“We have many of the tools in place -- from Web 2.0 technologies… to unstructured data search software and the Semantic Web -- to tame the digital universe. Done right, we can turn information growth into economic growth.”<br />-- “The Diverse and Exploding Digital Universe,” (IDC, 2008)<br />
  19. 19. Text mining: from information to intelligence<br />Text mining enables smarter search that better responds to user goals, e.g., answers –<br />
  20. 20. From Web 2.0 to Web 3.0<br />For even better findability:<br />“The Semantic Web is a web of data, in some ways like a global database.”<br /> -- Tim Berners-Lee, 1998<br />Web 3.0 is Web 2.0 + the Semantic Web + semantic tools.<br />Recurring themes:<br /><ul><li>Semantically enriched content & search.
  21. 21. Linked Data.
  22. 22. Context sensitive.
  23. 23. Location aware.</li></li></ul><li>The Semantic Web vision<br />&quot;<br />An open-standards architure, coordinated by the W3C (World Wide Web Consortium)<br />Linked Data: “exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web.”<br />
  24. 24. Steps in the right direction…<br />
  25. 25. … and missteps<br />“Kind” = type, variety, not a sentiment.<br />Complete misclassification<br />External reference<br />Unfiltered duplicates<br />
  26. 26. Getting to Web 3.0<br />Text mining / analytics enables Web 3.0 and the Semantic Web.<br /><ul><li>Automated content categorization and classification.
  27. 27. Text augmentation: metadata generation, content tagging.
  28. 28. Information extraction to databases.
  29. 29. Exploratory analysis and visualization.</li></ul>Technical concepts:<br /><ul><li>Linked Data
  30. 30. RDF, SPARQL, OWL
  31. 31. RDFa, Microformats, eRDF</li></li></ul><li>Text mining: users’ perspective<br />I recently published a study report, “Text Analytics 2009: User Perspectives on Solutions and Providers.”<br />I estimated a $350 million global market in 2008, up 40% from 2007.<br />I relayed findings from a survey that asked…<br />
  32. 32. Primary applications<br />What are your primary applications where text comes into play?<br />
  33. 33. Analyzedtextual information<br />What textual information are you analyzing or do you plan to analyze?<br />Current users responded:<br />
  34. 34. Extracted information<br />Do you need (or expect to need) to extract or analyze:<br />
  35. 35. Overall satisfaction<br />Please rate your overall experience – your satisfaction – with text mining.<br />
  36. 36. Movingahead<br />Apply text mining to discover value in content.<br />Develop / improve metadata and taxonomies.<br />Adopt semantic technologies -- for content publishing and for user interactions -- to boost flexibility, findability, and profitability.<br />And understand your audience:<br />“By focusing on the fundamental aspects of the consumers’ online behavior -- not just current best practices -- companies will be better prepared when Web 2.0+ morphs into Web 3.0 and beyond.”<br /> -- Donna L. Hoffman, UC Riverside, in the McKinsey Quarterly<br />
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