NOVA Data Science Meetup 1/19/2017 - Presentation 2
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Statistical NLP By Mona Diab
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NOVA Data Science Meetup 1/19/2017 - Presentation 2
- 1. Statistical NLP Mona Diab George Washington University
- 2. Who am I? • Prof in CS department working on issues of big data, data science, natural language processing • mtdiab@gwu.edu • Check out my research @ – www.seas.gwu.edu/~mtdiab • NLP lab @gw – Care4lang1.seas.gwu.edu
- 3. “Every 2 days we produce as much information as we did from the beginning of time till 2003” “Big Data refers to our ability to make use of the ever-increasing volumes of data.” “…everything we do is increasingly leaving a digital trace (or data), which we (and others) can use and analyze.” Bernard Marr
- 4. The Dream • It’d be great if machines could • Process our email (usefully) • Translate languages accurately • Help us manage, summarize, and aggregate information • Use speech as a UI (when needed) • Talk to us / listen to us • But they can’t: • Language is complex, ambiguous, flexible, and subtle • Good solutions need linguistics and machine learning knowledge Slide courtesy of Heng Ji
- 5. Heterogeneous BigData MartinLockheed 3,000 workers to furlough amid #USGovernmentShutdown The Patient Protection and Affordable Care Act (PPACA),[1] commonly called the Affordable Care Act (ACA) or Obamacare, is a United States federal statute signed into law by President Barack Obama on March 23, 2010. The U.S. Congress, still in partisan deadlock over Republican efforts to halt President Barack Obama's healthcare reforms, was on the verge of shutting down most of the U.S. government starting on Tuesday morning. NSF and NIST are temporarily closed because the Government entered a period of partial shutdown. President Obama's 70-minute White House meeting late Wednesday afternoon with congressional leaders including House Speaker John Boehner, did nothing to help end the impasse.
- 6. Mystery • What’s now impossible for computers (and any other species) to do is effortless for humans ✕ ✕ ✓
- 7. NLP to the rescue!
- 8. What is NLP? • Fundamental goal: deep understanding of broad language use • not just string processing or keyword matching!
- 9. What is NLP/CL? • NLP: Natural Language Processing – Is the field of making computers process natural language • Does process entail understand? • CL: Computational Linguistics – Is the field of using computers to understand (natural) language • Natural Language? – Refers to the language spoken by people, e.g. English, Japanese, Swahili, as opposed to artificial languages, like C++, Java, etc.
- 10. What is NLP? • Computers using and processing natural language input (data) and producing useful information, could be natural language output/or structured data • Software that can recognize, analyze and generate text and speech • Typically NLP refers to processing unstructured data – text in free form (unstructured text) • Contrast to Structured data refers to information in “tables” – Typically allows numerical range and exact match (for text) queries, e.g.,Salary < 60000 AND Manager = Smith, should return Turner, Ian Employee Manager Salary Smith, John David, Richard $80,000 Turner, Ian Smith, John $59,000 Huang, Chang Smith, John $69,000
- 11. 11 Unstructured (text) vs. structured (database) data in 1996 0 20 40 60 80 100 120 140 160 Data volume Market Cap Unstructured Structured
- 12. 12 Unstructured (text) vs. structured (database) data 0 20 40 60 80 100 120 140 160 Data volume Market Cap Unstructured Structured
- 13. Goals of NLP/CL • Model Human Language Processing • Analyze Human Language • Facilitate Human Language Communication via Automated Tools
- 14. Why NLP? • kJfmmfj mmmvvv nnnffn333 • Uj iheale eleee mnster vensi credur • Baboi oi cestnitze • Coovoel2^ ekk; ldsllk lkdf vnnjfj? • Fgmflmllk mlfm kfre xnnn! • Can you READ this? You, yes you!
- 15. Computers Lack Knowledge! • Computers “see” text in English/Arabic/French the same way you saw the previous slide! • People have no trouble understanding language – Common sense knowledge – Reasoning capacity – Experience • However, Computers have – No common sense knowledge – No reasoning capacity Unless we teach them!
- 16. Why Should You Care? • An enormous amount of knowledge is now available in machine readable form as natural language text • Conversational agents are becoming an important form of human-computer communication • Much of human-human communication is now mediated by computers • Very cool stuff! And with lots of commercial interest. Adapted from Speech and Language Processing - Jurafsky and MarJn
- 17. Why NLP? • Applications for processing large amounts of texts (BIG DATA) require NLP expertise • Classify text into categories • Index and search large texts • Automatic machine translation • Speech understanding – Understand phone conversations • Information extraction – Extract useful information from resumes • Automatic summarization – Condense 1 book into 1 page • Question answering • Knowledge acquisition • Text generation / dialogs
- 18. Who uses NLP Commercial World
- 19. Why is NLP intriguing? • NLP has an AI aspect to it – We’re often dealing with ill-defined problems – We don’t often come up with exact solutions/ algorithms – We can’t let either of those facts get in the way of making progress
- 20. NLP in CS taxonomy Computers Artificial Intelligence AlgorithmsDatabases Networking Robotics SearchNatural Language Processing Information Retrieval Machine Translation Language Analysis Semantics Parsing
- 21. The Challenge • Language is complex with infinite possible constructions • Good news is that there are patterns as the symbol set is finite, but the patterns are latent • Abundance of raw data
- 22. Why is NLP hard? Some Headlines… • Police Begin Campaign To Run Down Jaywalkers • Iraqi Head Seeks Arms • Enraged Cow Injures Farmer With Ax • Teacher Strikes Idle Kids • Squad Helps Dog Bite Victim • Red Tape Holds Up New Bridges • Hospitals Are Sued by 7 Foot Doctors • Court to Try Shooting Defendant • Local High School Dropouts Cut in Half
- 23. How can a machine understand these differences? • Get the cat with the gloves.
- 24. Ambiguous Spoken Example I made her duck • I cooked waterfowl for her • I cooked the waterfowl that belongs to her • I created the ceramic duck she owns • I caused her to quickly lower her head • And more….
- 25. Example … continued! I made her duck maid Eye Speech recognition cook create Word Sense Disambiguation Syntactic parsing Verb noun Part of Speech Tagging
- 26. Linguistics • It is the study of the science of human language • How the mind comes up with language
- 27. Levels of Language Description • 6 basic levels (more or less explicitly present in most theories): – and beyond (pragmatics/logic/...) – meaning (semantics) – (surface) syntax – morphology – phonology – phonetics/orthography • Each level has an input and output representation – output from one level is the input to the next (upper) level – sometimes levels might be skipped (merged) or split
- 28. The Steps in NLP Discourse Pragmatics Semantics Syntax Morphology **we can go up, down and up and down and combine steps too!! **every step is equally complex
- 29. The View: Ambiguity • All 6 levels of linguistic knowledge require resolving ambiguity • Ambiguity results from the existence of multiple possibilities for each level
- 30. Ambiguity • Computational linguists are obsessed with ambiguity • Ambiguity is a fundamental problem of computational linguistics • Resolving ambiguity is a crucial goal
- 31. non---standard English Great job @jusJnbieber! Were SOO PROUD of what youve accomplished! U taught us 2 #neversaynever & you yourself should never give up either♥ Adapted from Speech and Language Processing --- Jurafsky and Mar<n Why else is natural language understanding difficult?
- 32. non---standard English Great job @jusJnbieber! Were SOO PROUD of what youve accomplished! U taught us 2 #neversaynever & you yourself should never give up either♥ segmenta5on issues the New York---New Haven Railroad Adapted from Speech and Language Processing --- Jurafsky and MarJn the New York---New Haven Railroad Why else is natural language understanding difficult?
- 33. non---standard English Great job @jusJnbieber! Were SOO PROUD of what youve accomplished! U taught us 2 #neversaynever & you yourself should never give up either♥ segmenta5on issues idioms dark horse get cold feet lose face throw in the towel the New York---New Haven Railroad Adapted from Speech and Language Processing --- Jurafsky and MarJn the New York---New Haven Railroad Why else is natural language understanding difficult?
- 34. non---standard English Great job @jusJnbieber! Were SOO PROUD of what youve accomplished! U taught us 2 #neversaynever & you yourself should never give up either♥ segmenta5on issues idioms dark horse get cold feet lose face throw in the towel neologisms unfriend Retweet bromance the New York---New Haven Railroad Adapted from Speech and Language Processing --- Jurafsky and MarJn the New York---New Haven Railroad Why else is natural language understanding difficult?
- 37. non---standard English Great job @jusJnbieber! Were SOO PROUD of what youve accomplished! U taught us 2 #neversaynever & you yourself should never give up either♥ segmenta5on issues idioms dark horse get cold feet lose face throw in the towel neologisms unfriend Retweet bromance tricky en5ty names Where is A Bug’s Life playing … Let It Be sold millions… … a muta<on on the for gene … world knowledge Mary and Sue are sisters. Mary and Sue are mothers. But that’s what makes it fun! the New York---New Haven Railroad Adapted from Speech and Language Processing --- Jurafsky and MarJn the New York---New Haven Railroad Why else is natural language understanding difficult?
- 38. Making progress on this problem… • The task is difficult! What tools do we need? – Knowledge about language – Knowledge about the world – A way to combine knowledge sources • How we generally do this: – probabilistic models built from language data • P(“maison” → “house”) high • P(“L’avocat général” → “the general avocado”) low – Luckily, rough text features can often do half the job.
- 39. CL Toolkit • Knowledge of Linguistics, i.e. NLPers call them features!! • State Machines – Finite state automata, transducers • Formal Rule Systems – Regular Grammars, Context Free Grammars • Logic – First order logic, predicate calculus • Probability Theory – Associating probabilities with the previous machinery • Machine Learning Tools – Learning automatically from representations, play a very important role in cases where we don’t have good explanations of why things happen the way they do • Performance Metrics – Well defined evaluation metrics for different tasks
- 40. Major Topics 1. Words 2. Syntax 3. Meaning 4. Discourse 5. Applications exploiting each
- 41. Models and Algorithms • By models we mean the formalisms that are used to capture the various kinds of linguistic knowledge we need. • Algorithms are then used to manipulate the knowledge representations needed to tackle the task at hand.
- 42. Models • Finite state machines • Linguistic Rules • Markov models • Alignment • Vector space model of word and document meaning • Logical formalisms • Network models
- 43. Algorithms • Rule-based – Symbolic Parsers and morphological analyzers – Finite state automata • Probabilistic/statistical – Learned from observation of (labeled) data – Predicting new data based on old – Machine learning
- 44. Algorithms • Many of the algorithms that we’ll study will turn out to be transducers; algorithms that take one kind of structure as input and output another • Unfortunately, ambiguity makes this process difficult • This leads us to employ algorithms that are designed to handle ambiguity of various kinds • State-space search paradigm: To manage the problem of making choices during processing when we lack the information needed to make the right choice
- 45. Machine Learning Machine learning based classifiers that are trained to make decisions based on (implicitly or explicitly modeled) features from context Simple Classifiers: Naïve Bayes Logistic Regression (MaxEnt) Decision Trees Neural Networks Sequence Models: Hidden Markov Models Maximum Entropy Markov Models Conditional Random Fields Recursive Neural Networks (RNNs, LSTMs)
- 46. Approaching the challenge • Divide & Conquer – Break the problem into smaller problems • Throw state of the art techniques at the smaller problems • Keep your fingers crossed!!
- 47. NLP Categories • Applications • Word counters (wc in UNIX) • Spell Checkers, grammar checkers • Predictive Text on mobile handsets • Machine Translation (MT) • Information Retrieval (IR) • Automatic Speech Recognition (ASR) • Optical Character Recognition (OCR) • Automatic Summarization, Speech Synthesis, etc. • Enabling Technologies – Tokenization – Part-of-Speech Tagging – Syntactic Parsing – Lemmatization – Word Sense Disambiguation, etc.
- 48. • Alan Turing was British pioneering computer scientist, mathematician, logician, and cryptanalyst. He is widely considered the Father of Computer Science. • The movie Imitation Game is about him. • The Turing test is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine that is designed to generate human-‐ like responses. Turing Test Courtesy of Nizar Habash
- 49. Current Real-World Applications • Search: very large corpora, e.g. Google • Information Extraction: relevant information to a task • Sentiment analysis: restaurant or movie reviews • Summarizing very large amounts of text or speech: e.g. your email, the news, voicemail • Translating between one language and another: e.g. Google Translate, Babelfish • Dialogue systems: e.g. chatbots, Amtrak’s ‘Julie’ • Question answering: e.g. IBM’s Watson Jeopardy!, DARPA who/what/where…, Ask Jeeves • Even more: speech processing, common sense knowledge, text categorization, web monitoring, etc.
- 52. Machine Translation • Basic types of Machine Translation – Text to Text Machine Translations – Speech to Speech Machine Translations • To date, majority of approaches have targeted rich language pairs (with lots of automated resources) – No Swahili-German systems • Current approaches are statistical, learning from existing translations (parallel data collections) • Reasonable performance due significant funding
- 56. Blog Analytics • Data-mining of blogs, discussion forums, message boards, user groups, and other forms of user generated media – Product marketing information – Political opinion tracking – Social network analysis – Buzz analysis (what’s hot, what topics are people talking about right now).
- 57. Livejournal.com: I, me, my on or after Sep 11, 2001 o30-n5 o16-o22 o2-o8 s24 s22 s20 s18 s16 s14 s12 B 7.2 7.0 6.8 6.6 6.4 6.2 6.0 5.8 Graph from Pennebaker slides Cohn, Mehl, Pennebaker. 2004. LinguisJc markers of psychological change surrounding September 11, 2001. Psychological Science 15, 10: 687-693.
- 58. September 11 LiveJournal.com study: We, us, our o30-n5 o16-o22 o2-o8 s24 s22 s20 s18 s16 s14 s12 B 1.1 1.0 .9 .8 .7 .6 .5 Cohn, Mehl, Pennebaker. 2004. LinguisJc markers of psychological change surrounding September 11, 2001. Psychological Science 15, 10: 687-693. Graph from Pennebaker slides
- 59. Sentiment Analysis • Movie Review Mining – User1: The Matrix rocked, I simply loved it…. – User2: Really, that Keanu Reaves gets on my nerves, he is too robotic – User1: it was way deep, it obviously went over your head! – User2: I think it GOT INTO ur head J • What do you think User1 and User2’s sentiments are toward the movie? – User1 – User2 • What do you think the sentiment of User2 toward User1 is?
- 60. Sentiment Analysis • Movie Review Mining – User1: The Matrix rocked, I simply loved it…. – User2: Really, that Keanu Reaves gets on my nerves, he is too robotic – User1: it was way deep, it obviously went over your head! – User2: I think it GOT INTO ur head J • What do you think User1 and User2’s sentiments are toward the movie? – User1 – User2 • What do you think the sentiment of User2 toward User1 is?
- 61. What about positive restaurant reviews? Sex, Drugs, and Dessert • sexy food • seductively seared fois gras — addicted to pepper shooters — garlic noodles… my drug of choice — the fries are like crack
- 62. Let’s Zoom in • Beyond Sentiment – Perspective Identification • Why would you need that? – Polling – Detection of bias • How would you go about doing it? – Sentiment analysis – Opinion mining – Cognitive state (belief)
- 63. Dialogue Systems • User: Can you book me a flight from New York City to Washington? • System: Is your destination Washington state? • User: No I mean Washington DC • System: When would you like to fly? • Example: Amtrak’s Julie system
- 64. Tell me what's bothering you. hbp://www.masswerk.at/elizabot/eliza.html
- 65. Eliza • Eliza is a mock Rogerian psychotherapist – Rogerian psychotherapy, or person-‐centered therapy, is a form of talk-‐psychotherapy developed by psychologist Carl Rogers in the 1940s & 1950s. • The original program was described by Joseph Weizenbaum in 1966. • Primitive natural language processing using simple scripts and regular expressions • Chatterbots or chatbots • Eliza effect – the tendency to unconsciously assume computer behaviors are analogous to human behaviors. • Turing test
- 66. • Men are all alike. • IN WHAT WAY? • They're always bugging us about something or other. • CAN YOU THINK OF A SPECIFIC EXAMPLE? • Well, my boyfriend made me come here. • YOUR BOYFRIEND MADE YOU COME HERE • He says I'm depressed much of the time. • I AM SORRY TO HEAR YOU ARE DEPRESSED • It's true. I am unhappy • DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY • I need some help, that much seems certain. • WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP • Perhaps I could learn to get along with my mother. • TELL ME MORE ABOUT YOUR FAMILY • My mother takes care of me. • WHO ELSE IN YOUR FAMILY TAKES CARE OF YOU • My father. • YOUR FATHER • You are like my father in some ways. Original Chatting Program Eliza
- 67. s/.* YOU ARE (depressed|sad) .*/I AM SORRY TO HEAR YOU ARE 1/ s/.* YOU ARE (depressed|sad) .*/WHY DO YOU THINK YOU ARE 1/ s/.* all .*/IN WHAT WAY/ s/.* always .*/CAN YOU THINK OF A SPECIFIC EXAMPLE/ Eliza-style regular expressions Step 1: replace first person with second person references s/bI(’m| am)b /YOU ARE/g s/bmyb /YOUR/g S/bmineb /YOURS/g Step 2: use additional regular expressions to generate replies Step 3: use scores to rank possible transformations
- 68. • Let’s chat with Mitsuku! • http://www.mitsuku.com • Loebner prize winner 2013, runner up 2015 – Modern form of the Turing test for Artificial Intelligence Mitsuku Slide courtesy of Nizar Habash
- 70. Question Answering: IBM’s Watson • Won Jeopardy on February 16, 2011! 70 WILLIAM WILKINSON’S “AN ACCOUNT OF THE PRINCIPALITIES OF WALLACHIA AND MOLDOVIA” INSPIRED THIS AUTHOR’S MOST FAMOUS NOVEL Bram Stoker
- 71. §§ Capture the imagination – The Next Deep Blue §§ Engage the scientific community – Envision new ways for computers to impact society & science – Drive important and measurable scientific advances §§ Be Relevant to IBM Customers – Enable better, faster decision making over unstructured and structured content – Business Intelligence, Knowledge Discovery and Management, Government, Compliance, Publishing, Legal, Healthcare, Business Integrity, Customer Relationship Management, Web Self-Service, Product Support, etc. A Grand Challenge Opportunity © 2011 IBM CorporaJon
- 72. Real Language is Real Hard – A finite, mathematically well-defined search space – Limited number of moves and states – Grounded in explicit, unambiguous mathematical rules – Ambiguous, contextual and implicit – Grounded only in human cognition – Seemingly infinite number of ways to express the same meaning © 2011 IBM CorporaJon Chess Human Language
- 73. Easy Questions? Serial Number Type Invoice # 45322190-AK LapTop INV10895 David Jones David Jones = ln((12,546,798 * π)) ^ 2 / 34,567.46 = 0.00885 Select Payment where Owner=“David Jones” and Type(Product)=“Laptop”, Owner Serial Number David Jones 45322190-AK Invoice # Vendor Payment INV10895 MyBuy $104.56 Dave Jones David Jones ≠ © 2011 IBM CorporaJon
- 74. Hard Questions? Computer programs are natively explicit, fast and exacting in their calculation over numbers and symbols….But Natural Language is implicit, highly contextual, ambiguous and often imprecise. §§ Wherewas X born? One day, from among his city views of Ulm, Otto chose a water color to send to Albert Einstein as a remembrance of Einstein´s birthplace. §§ X ran this? If leadership is an art then surely Jack Welch has proved himself a master painter during his tenure at GE. Person Birth Place A. Einstein ULM Person Organization J. Welch GE Structured Unstructured © 2011 IBM CorporaJon
- 75. Automatic Open-Domain Question Answering A Long-Standing Challenge in Artificial Intelligence to emulate human expertise © 2011 IBM CorporaJon 7 §§ Given – Rich Natural LanguageQuestions – Over a Broad Domain ofKnowledge §§ Deliver – Precise Answers:Determine what is being asked & give precise response – Accurate Confidences: Determine likelihood answer iscorrect – Consumable Justifications: Explain why the answer isright – Fast Response Time: Precision & Confidence in <3seconds
- 76. Information Retrieval • Very successful enterprise: Google, Bing, Yahoo, Altavista • General model: given a huge collection of texts (document collection), given a query – Task: find specific documents that are relevant to the given query – How: Create an index, like the index in a book to look up the information, predominant approaches include vector space models
- 77. Information Extraction Subject: curriculum meeting Date: January 15, 2012 To: Dan Jurafsky Hi Dan, we’ve now scheduled the curriculum meeting. It will be in Gates 159 tomorrow from 10:00-11:30. -Chris Create new Calendar entry Event: Curriculum mtg Date: Jan-16-2012 Start: 10:00am End: 11:30am Where: Gates 159
- 78. Information Extraction • nice and compact to carry! • since the camera is small and light, I won't need to carry around those heavy, bulky professional cameras either! • the camera feels flimsy, is plastic and very light in weight you have to be very delicate in the handling of this camera78 Size and weight Abributes: zoom affordability size and weight flash ease of use ✓ ✗ ✓
- 80. • Thanks for listening!! • Questions?
- 81. Reminder of who I amJ • Prof in CS department working on issues of big data, data science, natural language processing • mtdiab@gwu.edu • Check out my research @ – www.seas.gwu.edu/~mtdiab • NLP lab @gw – Care4lang1.seas.gwu.edu