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Predictive Text Analytics Seth Grimes Alta Plana Corporation 301-270-0795 --  http://altaplana.com --  @SethGrimes Predictive Analytics World October 20, 2009
Context Counts
What is Analytics? http://www.tropicalisland.de/NYC_New_York_Brooklyn_Bridge_from_World_Trade_Center_b.jpg x(t) = t y(t) = ½ a (e t/a  + e -t/a ) = acosh(t/a) http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg c:emparcus.pdf c:emparcus.pdf
What is Analytics? "SUMLEV","STATE","COUNTY","STNAME","CTYNAME","YEAR","POPESTIMATE", 50,19,1,"Iowa","Adair County",1,8243,4036,4207,446,225,221,994,509 50,19,1,"Iowa","Adair County",2,8243,4036,4207,446,225,221,994,509 50,19,1,"Iowa","Adair County",3,8212,4020,4192,442,222,220,987,505 50,19,1,"Iowa","Adair County",4,8095,3967,4128,432,208,224,935,488 50,19,1,"Iowa","Adair County",5,8003,3924,4079,405,186,219,928,495 50,19,1,"Iowa","Adair County",6,7961,3892,4069,384,183,201,907,472 50,19,1,"Iowa","Adair County",7,7875,3855,4020,366,179,187,871,454 50,19,1,"Iowa","Adair County",8,7795,3817,3978,343,162,181,841,439 50,19,1,"Iowa","Adair County",9,7714,3777,3937,338,159,179,805,417
www.stanford.edu/%7ernusse/wntwindow.html Axin and Frat1 interact with dvl and GSK, bridging Dvl to GSK in Wnt-mediated regulation of LEF-1. Wnt proteins transduce their signals through dishevelled (Dvl) proteins to inhibit glycogen synthase kinase 3beta (GSK), leading to the accumulation of cytosolic beta-catenin and activation of TCF/LEF-1 transcription factors. To understand the mechanism by which Dvl acts through GSK to regulate LEF-1, we investigated the roles of Axin and Frat1 in Wnt-mediated activation of LEF-1 in mammalian cells. We found that Dvl interacts with Axin and with Frat1, both of which interact with GSK. Similarly, the Frat1 homolog GBP binds Xenopus Dishevelled in an interaction that requires GSK. We also found that Dvl, Axin and GSK can form a ternary complex bridged by Axin, and that Frat1 can be recruited into this complex probably by Dvl. The observation that the Dvl-binding domain of either Frat1 or Axin was able to inhibit Wnt-1-induced LEF-1 activation suggests that the interactions between Dvl and Axin and between Dvl and Frat may be important for this signaling pathway. Furthermore, Wnt-1 appeared to promote the disintegration of the Frat1-Dvl-GSK-Axin complex, resulting in the dissociation of GSK from Axin. Thus, formation of the quaternary complex may be an important step in Wnt signaling, by which Dvl recruits Frat1, leading to Frat1-mediated dissociation of GSK from Axin. www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Retrieve&list_uids=10428961&dopt=Abstract The “Unstructured Data” Challenge
Modelling Text ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the auto-abstract.” H.P. Luhn,  The Automatic Creation of Literature Abstracts ,  IBM Journal , 1958.
Text Modelling Document content can be considered an unordered “bag of words.” Particular documents are points in a high-dimensional vector space. Salton, Wong & Yang, “A Vector Space Model for Automatic Indexing,” November 1975.
Text Modelling ,[object Object],[object Object],[object Object],[object Object],[object Object],http://en.wikipedia.org/wiki/Term-document_matrix I like hate databases D1 1 1 0 1 D2 1 0 2 1
Text Modelling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Text Modelling ,[object Object],[object Object],[object Object],[object Object]
Semantics “ 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.” -- Hans Peter Luhn, 1958
New York Times , September 8, 1957 Anaphora / coreference:  “They”
Semantics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
 
 
 
 
Text Analytics ,[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Text Analytics? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Text Analytics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Text ,[object Object],[object Object],[object Object],[object Object],[object Object],http://en.wikipedia.org/wiki/File:ITap_on_Motorola_C350.jpg
Predictive Text ,[object Object],[object Object]
Predictive Text Analytics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],Predictive Analytics, Text Sources
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sources
Search++ Search is typically answer #1.
Beyond Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Knowledge Discovery
Visualizing Interrelationships
Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Text Analytics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Document processing -- This slide and the next show dynamic, clustered search results from Grokker (now gone)…
… with a zoomable display. Clustering here identifies cohesive groupings of retrieved documents.
 
Sentiment Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sentiment Analysis
Steps in the Right Direction
 
Unfiltered duplicates External reference “ Kind” = type, variety, not  sentiment. Naïve misclassification ... and Missteps
[object Object],[object Object],Beyond Polarity
Happy Sad Angry Energetic Confused Aggravated Bouncy Crappy Angry Happy Crushed Bitchy Hyper Depressed Enraged Cheerful Distressed Infuriated Ecstatic Envious Irate Excited Gloomy Pissed off Jubilant Guilty Giddy Intimidated Giggly Jealous Lonely Rejected Sad Scared ----------------------- The three prominent mood groups that emerged from K-Means Clustering on the set of  LiveJournal  mood labels.
Predictive Text Analytics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Seth Grimes Alta Plana Corporation 301-270-0795 –  http://altaplana.com @SethGrimes

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Predictive Text Analytics

  • 1. Predictive Text Analytics Seth Grimes Alta Plana Corporation 301-270-0795 -- http://altaplana.com -- @SethGrimes Predictive Analytics World October 20, 2009
  • 3. What is Analytics? http://www.tropicalisland.de/NYC_New_York_Brooklyn_Bridge_from_World_Trade_Center_b.jpg x(t) = t y(t) = ½ a (e t/a + e -t/a ) = acosh(t/a) http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg c:emparcus.pdf c:emparcus.pdf
  • 4. What is Analytics? "SUMLEV","STATE","COUNTY","STNAME","CTYNAME","YEAR","POPESTIMATE", 50,19,1,"Iowa","Adair County",1,8243,4036,4207,446,225,221,994,509 50,19,1,"Iowa","Adair County",2,8243,4036,4207,446,225,221,994,509 50,19,1,"Iowa","Adair County",3,8212,4020,4192,442,222,220,987,505 50,19,1,"Iowa","Adair County",4,8095,3967,4128,432,208,224,935,488 50,19,1,"Iowa","Adair County",5,8003,3924,4079,405,186,219,928,495 50,19,1,"Iowa","Adair County",6,7961,3892,4069,384,183,201,907,472 50,19,1,"Iowa","Adair County",7,7875,3855,4020,366,179,187,871,454 50,19,1,"Iowa","Adair County",8,7795,3817,3978,343,162,181,841,439 50,19,1,"Iowa","Adair County",9,7714,3777,3937,338,159,179,805,417
  • 5. www.stanford.edu/%7ernusse/wntwindow.html Axin and Frat1 interact with dvl and GSK, bridging Dvl to GSK in Wnt-mediated regulation of LEF-1. Wnt proteins transduce their signals through dishevelled (Dvl) proteins to inhibit glycogen synthase kinase 3beta (GSK), leading to the accumulation of cytosolic beta-catenin and activation of TCF/LEF-1 transcription factors. To understand the mechanism by which Dvl acts through GSK to regulate LEF-1, we investigated the roles of Axin and Frat1 in Wnt-mediated activation of LEF-1 in mammalian cells. We found that Dvl interacts with Axin and with Frat1, both of which interact with GSK. Similarly, the Frat1 homolog GBP binds Xenopus Dishevelled in an interaction that requires GSK. We also found that Dvl, Axin and GSK can form a ternary complex bridged by Axin, and that Frat1 can be recruited into this complex probably by Dvl. The observation that the Dvl-binding domain of either Frat1 or Axin was able to inhibit Wnt-1-induced LEF-1 activation suggests that the interactions between Dvl and Axin and between Dvl and Frat may be important for this signaling pathway. Furthermore, Wnt-1 appeared to promote the disintegration of the Frat1-Dvl-GSK-Axin complex, resulting in the dissociation of GSK from Axin. Thus, formation of the quaternary complex may be an important step in Wnt signaling, by which Dvl recruits Frat1, leading to Frat1-mediated dissociation of GSK from Axin. www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Retrieve&list_uids=10428961&dopt=Abstract The “Unstructured Data” Challenge
  • 6.
  • 7. “ Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the auto-abstract.” H.P. Luhn, The Automatic Creation of Literature Abstracts , IBM Journal , 1958.
  • 8. Text Modelling Document content can be considered an unordered “bag of words.” Particular documents are points in a high-dimensional vector space. Salton, Wong & Yang, “A Vector Space Model for Automatic Indexing,” November 1975.
  • 9.
  • 10.
  • 11.
  • 12. Semantics “ 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.” -- Hans Peter Luhn, 1958
  • 13. New York Times , September 8, 1957 Anaphora / coreference: “They”
  • 14.
  • 15.  
  • 16.  
  • 17.  
  • 18.  
  • 19.  
  • 20.  
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. Search++ Search is typically answer #1.
  • 30.
  • 33.
  • 34.
  • 35. Document processing -- This slide and the next show dynamic, clustered search results from Grokker (now gone)…
  • 36. … with a zoomable display. Clustering here identifies cohesive groupings of retrieved documents.
  • 37.  
  • 38.
  • 39.
  • 40. Steps in the Right Direction
  • 41.  
  • 42. Unfiltered duplicates External reference “ Kind” = type, variety, not sentiment. Naïve misclassification ... and Missteps
  • 43.
  • 44. Happy Sad Angry Energetic Confused Aggravated Bouncy Crappy Angry Happy Crushed Bitchy Hyper Depressed Enraged Cheerful Distressed Infuriated Ecstatic Envious Irate Excited Gloomy Pissed off Jubilant Guilty Giddy Intimidated Giggly Jealous Lonely Rejected Sad Scared ----------------------- The three prominent mood groups that emerged from K-Means Clustering on the set of LiveJournal mood labels.
  • 45.
  • 46. Seth Grimes Alta Plana Corporation 301-270-0795 – http://altaplana.com @SethGrimes

Notas del editor

  1. This course is, in essence, about the information enterprises have and how they use it and how they could better use it. First we look at enterprise information in light of business goals in order to characterize the “unstructured” information gap. We then look at how that information, or at least the textual variety, may be structured for use. Then we look at a few uses, at enriching search, surely one of today’s killer apps, and at enhancing business intelligence via search.