Search, Signals & Sense: An Analytics Fueled Vision

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Keynote presented by Seth Grimes at the Open Source Search Conference, October 2, 2012

Keynote presented by Seth Grimes at the Open Source Search Conference, October 2, 2012

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  • 1. Search, Signals & Sense: An Analytics Fueled VisionSeth Grimes@sethgrimes
  • 2. A Sense Making Story New York Times, September 30, 2012
  • 3. Valium: Starting a Chain of Connections New York Times, September 8, 1957
  • 4. H.P. LuhnBy H.P. Luhn, inIBM Journal,April, 1958http://altaplana.com/ibm-luhn58-LiteratureAbstracts.pdf
  • 5. Modelling Text Luhn’s analysis of Messengers of the Nervous System, a Scientific American article http://wordle.net, applied to the NY Times article“Statistical information derived from word frequency and distribution isused by the machine to compute a relative measure of significance, firstfor individual words and then for sentences. Sentences scoring highest insignificance are extracted and printed out to become the auto-abstract.” -- H.P. Luhn, The Automatic Creation of Literature Abstracts, IBM Journal, 1958.
  • 6. Luhn’s Example New York Times, September 8, 1957
  • 7. Close Reading
  • 8. Can Software Make the Connection? Mark Lombardi, George W. Bush, Harken Energy and Jackson Stephens, c. 1979-90, Detail
  • 9. There and Back Again: Modelling Text, 2The text content of a document 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.
  • 10. Modelling Text, 3We might construct a document-term matrix... • D1 = “I like databases” • D2 = “I hate hate databases” I like hate databases D1 1 1 0 1 D2 1 0 2 1 http://en.wikipedia.org/wiki/Term-document_matrixand use a weighting such as TF-IDF (term frequency– inverse document frequency)…in computing the cosine of the angle between weighted doc-vectors to determine similarity.
  • 11. Modelling Text, 4In the form of query-document similarity, this is Information Retrieval 101. • See, for instance, Salton & Buckley, “Term-Weighting Approaches in Automatic Text Retrieval,” 1988. • A useful basic tech paper: Russ Albright, SAS, “Taming Text with the SVD,” 2004.Given the complexity of human language, statistical models may fall short. “Reading from text in general is a hard problem, because it involves all of common sense knowledge.” -- Expert systems pioneer Edward A. Feigenbaum
  • 12. From Text to Data: FeaturesAnalytical methods make text tractable. Latent semantic indexing utilizing singular value decomposition for term reduction / feature selection.Classification technologies / methods: • Naive Bayes. • Support Vector Machine. • K-nearest neighbor.
  • 13. “Reading from Text is a Hard Problem” Eugène Delacroix, St. Michael Defeats the Devil Thus the Orb he roamdWith narrow search; and with inspection deep Considerd every Creature, which of all Most opportune might serve his Wiles. -- John Milton, Paradise Lost
  • 14. Data, Search, Analysis, and Discovery Eugène Delacroix, St. Michael Defeats the Devil DataFor Spacefeatures Analysis Thus the Orb he roamd With narrow search; and with inspection deep Considerd every Creature, which of all Intent, Most opportune might serve his Wiles. Goals -- John Milton, Paradise Lost
  • 15. The User Interface“Search is the UI for data today.” -- Grant Ingersoll, Chief Scientist, LucidWorks Quoted by Gil Press in Forbes, “LucidWorks: Bringing Search to Big Data” http://www.forbes.com/sites/gilpress/2012/09/24/lucidworks-bringing-search-to-big-data/What’s beyond?
  • 16. Search and Sensemaking“It is convenient to divide the entireinformation access process into twomain components: information retrievalthrough searching and browsing, andanalysis and synthesis of results. Thisbroader process is often referred to inthe literature as sensemaking.Sensemaking refers to an iterativeprocess of formulating a conceptualrepresentation from of a large volumeof information. Search plays only onepart in this process.” -- Marti Hearst, 2009 http://searchuserinterfaces.com/
  • 17. Senseless SearchNew but old: Dumb and siloed
  • 18. Searcher Supplied SenseBetter?
  • 19. Siloed signals.More better?
  • 20. Semantic Search EnginesMeh.
  • 21. Clustered ClarityCarrot2.(open source)
  • 22. Semanticized (Web) SearchGoogleKnowledgeGraph
  • 23. Search Fronted Analysis & Discovery Fusions, Signals
  • 24. Toward Semantic Search SensemakingOld Search SensemakingSearch on: keywords + identity, history & contextSources: content/type silos UnifiedIndexed: terms + metadata (properties)Returned: hit lists Categories / clusters / answers firstRelevance: PageRank (Inferred) intentPrevalence: plenty of new Plenty of established platforms with old(ish) search with new(ish) search capabilities, also wanna- bes.
  • 25. The Back EndPlatforms and ecosystems.APIs and services.Text and content analytics -- Discerns and extracts features including relationships from source materials. Features = entities, key-value pairs, concepts, topics, events, sentiment, etc. Provide (for) BI on content-sourced data.Data integration, record linkage, data fusion.
  • 26. Text+ Technology MashupsText/content analytics generates semantics to bridge search, BI, and applications, enabling next- generation information systems. Semantic search Information access (search + text) (search + text + BI)Search based Search BIapplications Integrated analytics(search + text + (text + BI)apps) Applica- Text analytics tions NextGen CRM, EFM, (inner circle) MR, marketing, …
  • 27. Analytical Assets (Open Source) >>> import nltk >>> sentence = """At eight oclock on Thursday morning... Arthur didnt feel very good.""" >>> tokens = nltk.word_tokenize(sentence) >>> tokens [At, eight, "oclock", on, Thursday, morning, Arthur, did, "nt", feel, very, good, .] >>> tagged = nltk.pos_tag(tokens) >>> tagged[0:6] [(At, IN), (eight, CD), ("oclock", JJ), (on, IN), (Thursday, NNP), (morning, NN)] http://nltk.org/tm: Text Mining PackageA framework for text miningapplications within R.
  • 28. A Big Data Analytics Architecturehttp://hpccsystems.com/ (GNU Affero GPL) http://www.geeklawblog.com/2011/12/lexis-advance-platform-launch-two.html
  • 29. Commercial (Non-OS) Solutions Plug In
  • 30. Drivers and TrendsSocial media! … and personal-social-enterprise integration.Via-API cloud services.Big Data (even if you don’t like the term). Volume and velocity mean new analytical approaches. Variety: new types and a new fusion imperative.Sentiment: Mood, opinions, emotions, intent.Question answering.
  • 31. Text Tech InitiativesNow and near future. • Broader & deeper international language support. • Sentiment analysis, beyond polarity. Emotions, intent signals. etc. • Identity resolution & profile extraction. Online-social-enterprise data integration. • Semantic data integration, Complex Data. • Speech analytics. • Discourse analysis. Because isolated messages are not conversations. • Rich-media content analytics. • Augmented reality; new human-computer interfaces.
  • 32. Personal. Mobile. Intelligent?http://timoelliott.com/blog/2010/10/sap-businessobjects-augmented-explorer-now-available-resources-to-test-it.html
  • 33. A Focus on Information & ApplicationsNow and near future. • Signal detection. Sentiment, emotion, identity, intent. • Semanticized applications. Linkable, mashable, enrichable. • Rich information. Context sensitive, situational.Σ = Sensemaking.
  • 34. Onward… to Q&A
  • 35. Search, Signals & Sense: An Analytics Fueled VisionSeth Grimes@sethgrimes