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Towards Google-like Search on Spoken Documents with Zero Resources How to get something from nothing in a language that

Towards Google-like Search on Spoken Documents with Zero Resources How to get something from nothing in a language that you’ve never heard of. Speech/EE Meeting. Language/CS Meeting. Degree of difficulty. Some queries are harder than others More bits? (Long tail: long/infrequent)

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Towards Google-like Search on Spoken Documents with Zero Resources How to get something from nothing in a language that

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  1. Towards Google-like Search on Spoken Documents with Zero ResourcesHow to get something from nothingin a language that you’ve never heard of Speech/EE Meeting Language/CS Meeting

  2. Degree of difficulty • Some queries are harder than others • More bits? (Long tail: long/infrequent) • Or less bits? (Big fat head: short/frequent) • Query refinement/Coping Mechanisms: • If query doesn’t work • Should you make it longer or shorter? • Solitaire Multi-Player Game: • Readers, Writers, Market Makers, etc. • Good Guys & Bad Guys: Advertisers & Spammers

  3. Spoken Web Search is Easy Compared to Dictation

  4. Entropy of Search Logs-How Big is the Web?- How Hard is Search? - With Personalization? With Backoff? Qiaozhu Mei†, Kenneth Church‡ † University of Illinois at Urbana-Champaign ‡ Microsoft Research

  5. Small How Bigis the Web?5B? 20B? More? Less? • What if a small cache of millions of pages • Could capture much of the value of billions? • Could a Big bet on a cluster in the clouds • Turn into a big liability? • Examples of Big Bets • Computer Centers & Clusters • Capital (Hardware) • Expense (Power) • Dev (Mapreduce, GFS, Big Table, etc.) • Sales & Marketing >> Production & Distribution • Goal: Maximize Sales (Not Inventory)

  6. Millions (Not Billions)

  7. Population Bound • With all the talk about the Long Tail • You’d think that the Web was astronomical • Carl Sagan: Billions and Billions… • Lower Distribution $$  Sell Less of More • But there are limits to this process • NetFlix: 55k movies (not even millions) • Amazon: 8M products • Vanity Searches: Infinite??? • Personal Home Pages << Phone Book < Population • Business Home Pages << Yellow Pages < Population • Millions, not Billions (until market saturates)

  8. It Will Take Decades to Reach Population Bound • Most people (and products) • don’t have a web page (yet) • Currently, I can find famous people • (and academics) • but not my neighbors • There aren’t that many famous people • (and academics)… • Millions, not billions • (for the foreseeable future)

  9. If there is a page on the web, And no one sees it, Did it make a sound? How big is the web? Should we count “silent” pages That don’t make a sound? How many products are there? Do we count “silent” flops That no one buys? Equilibrium: Supply = Demand

  10. Demand Side Accounting • Consumers have limited time • Telephone Usage: 1 hour per line per day • TV: 4 hours per day • Web: ??? hours per day • Suppliers will post as many pages as consumers can consume (and no more) • Size of Web: O(Consumers)

  11. How Big is the Web? • Related questions come up in language • How big is English? • Dictionary Marketing • Education (Testing of Vocabulary Size) • Psychology • Statistics • Linguistics • Two Very Different Answers • Chomsky: language is infinite • Shannon: 1.25 bits per character How many words do people know? What is a word? Person? Know?

  12. Chomskian Argument: Web is Infinite • One could write a malicious spider trap • http://successor.aspx?x=0 http://successor.aspx?x=1http://successor.aspx?x=2 • Not just academic exercise • Web is full of benign examples like • http://calendar.duke.edu/ • Infinitely many months • Each month has a link to the next

  13. How Bigis the Web?5B? 20B? More? Less? • More (Chomsky) • http://successor?x=0 • Less (Shannon) MSN Search Log 1 month  x18 More Practical Answer Comp Ctr ($$$$)  Walk in the Park ($) Millions (not Billions) Cluster in Cloud  Desktop  Flash

  14. Entropy (H) • Size of search space; difficulty of a task • H = 20  1 million items distributed uniformly • Powerful tool for sizing challenges and opportunities • How hard is search? • How much does personalization help?

  15. How Hard Is Search?Millions, not Billions • Traditional Search • H(URL | Query) • 2.8 (= 23.9 – 21.1) • Personalized Search • H(URL | Query, IP) • 1.2 (= 27.2 – 26.0) Personalization cuts H in Half!

  16. How Hard are Query Suggestions?The Wild Thing? C* Rice  Condoleezza Rice • Traditional Suggestions • H(Query) • 21 bits • Personalized • H(Query | IP) • 5 bits (= 26 – 21) Personalization cuts H in Half! Twice

  17. Conclusions: Millions (not Billions) • How Big is the Web? • Upper bound: O(Population) • Not Billions • Not Infinite • Shannon >> Chomsky • How hard is search? • Query Suggestions? • Personalization? • Cluster in Cloud ($$$$)  Walk-in-the-Park ($) • Goal: Maximize Sales (Not Inventory) Entropy is a great hammer

  18. Search Companies have massive resources (logs) • “Unfair” advantage: logs • Logs are the crown jewels • Search companies care so much about data collection that… • Toolbar: business case is all about data collection • The $64B question: • What (if anything) can we do without resources?

  19. Towards Google-like Search on Spoken Documents with Zero ResourcesHow to get something from nothingin a language that you’ve never heard of Speech/EE Meeting Language/CS Meeting

  20. Definitions ? • Towards: • Not there yet • Zero Resources: • No nothing (no knowledge of language/domain) • The next crisis will be where we are least prepared • No training data, no dictionaries, no models, no linguistics • Motivate a New Task: Spoken Term Discovery • Spoken Term Detection (Word Spotting): Standard Task • Find instances of spoken phrase in spoken document • Input: spoken phrase + spoken document • Spoken Term Discovery: Non-Standard task • Input: spoken document (without spoken phrase) • Output: spoken phrases (interesting intervals in document)

  21. The next crisis will be where we are least prepared ?

  22. RECIPE FOR REVOLUTION • 2 Cups of a long-standing leader • 1 Cup of an aggravated population • 1 Cup of police brutality • ½ teaspoon of video • Bake in YouTube for 2-3 months • Flambé with Facebook and sprinkle on some Twitter

  23. Longer is Easier • female • female encyclopedias /iy/ • male • male

  24. What makes an interval of speech interesting? • Cues from text processing: • Long (~ 1 sec such as “University of Pennsylvania”) • Repeated • Bursty (tf * IDF) • tf: lots of repetitions within documents • IDF: with relatively few repetitions across documents • Unique to speech processing: • Given-New: • First mention is articulated more carefully than subsequent • Dialog between two parties (A & B): multi-player game • A: utters an important phrase • B: what? • A: repeats the important phrase

  25. n2 Time & Space(Update: ASRU-2011) • But the constants are attractive • Sparsity • Redesigned algorithms to take advantage of sparsity • Median Filtering • Hough Transform • Line Segment Search • Approx Nearest Neighbors (ASRU-2012)

  26. NLP on Spoken Documents Without Automatic Speech Recognition Mark Dredze, Aren Jansen,Glen Coppersmith, Ken Church EMNLP-2010

  27. At least not for all NLP tasks We Don’t Need Speech Recognition To Process Speech

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