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LING/C SC/PSYC 438/538 Computational Linguistics

LING/C SC/PSYC 438/538 Computational Linguistics. Sandiway Fong Lecture 1: 8/21. Part 1. Administrivia. Where S SCI 224 When TR 12:30–1:45PM (Computer Lab). No Class Scheduled For Thursday October 18th Thursday November 22nd (Thanksgiving) Office Hours catch me after class, or

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LING/C SC/PSYC 438/538 Computational Linguistics

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  1. LING/C SC/PSYC 438/538Computational Linguistics Sandiway Fong Lecture 1: 8/21

  2. Part 1 • Administrivia

  3. Where S SCI 224 When TR 12:30–1:45PM (Computer Lab) No Class Scheduled For Thursday October 18th Thursday November 22nd (Thanksgiving) Office Hours catch me after class, or by appointment Location: Douglass 311 Administrivia

  4. Administrivia • Map • Office (Douglass) • Classroom (S SCI)

  5. Administrivia • Email • sandiway@email.arizona.edu • Homepage • http://dingo.sbs.arizona.edu/~sandiway • Lecture slides • available on homepage after each class • in both PowerPoint (.ppt) and Adobe PDF formats • animation: in powerpoint

  6. Course Objectives Theoretical Introduction to a broad selection of natural language processing techniques Survey course Practical Acquire some expertise Use of tools Parsing algorithms Write grammars and machines Administrivia

  7. Administrivia Reference Textbook • Speech and Language Processing, Jurafsky & Martin, Prentice-Hall 2000 • 21 chapters (900 pages) • Concepts, algorithms, heuristics • This course concentrates on the sentence level stuff • Sound/speech side • Prof. Y. Lin Speech Tech LING 578 (this semester) • Prof. Y. Lin Statistical NLP LING 539 (Spring 2008) • More advanced course • LING 581: Advanced Computational Linguistics • required for HLT Master’s Program students

  8. Administrivia • Laboratory Exercises • To run tools and write grammars • you need access to computational facilities • use your PC or Mac • run Windows, Linux or MacOSX • Homework exercises

  9. Administrivia • Grading • 3 homeworks • Exams • a mid-term • a final • mix of theoretical and practical exercises

  10. Homeworks Homeworks will be presented/explained in class (good chance toask questions) Please attempt homeworks early (then you can ask questions before the deadline) you have one week to do the homework (midnight deadline) (email submission to me) e.g. homework comes out on Thursday, it is due in my mailbox by next Thursday midnight Administrivia

  11. Administrivia • Homework Policy • You may discuss your homework with others • You must write up your homework by yourself • You must cite sources and references • Code of Academic Integrity • http://dos.web.arizona.edu/uapolicies/cai1.html • Late homeworks are subject to points deduction • Really late homeworks, e.g. a week late, will not be accepted • Emergencies and scheduled absences: inform instructor to make alternative arrangements

  12. Administrivia • Requirements: 438 vs. 538 538 = 438 + 1 classroom presentation of a selected chapter from the textbook + 438 extra credit homework and exam questions are obligatory

  13. Administrivia • Requirements: 538

  14. I’ll pass my laptop around ... Use PhotoBooth Fill in Excel spreadsheet Name PhotoBooth # Email Major Any programming expertise? Have a laptop? Knowledge of Linguistics? Class Questionnaire click on red button to take a picture of yourself

  15. Part 2 • Introduction

  16. Human Language Technology (HLT) • ... is everywhere • information is organized and accessed using language

  17. Human Language Technology (HLT) Beginnings • c. 1950 (just after WWII) • Electronic computers invented for • numerical analysis • code breaking Grand Challenges for Computers... Killer Apps: • Language comprehension tasks and Machine Translation (MT) References • Readings in Machine Translation • Eds. Nirenburg, S. et al. MIT Press 2003. • (Part 1: Historical Perspective) • Read Chapter 1 of the textbook • www.cs.colorado.edu/~martin/SLP/slp-ch1.pdf

  18. Human Language Technology (HLT) • Cryptoanalysis Basis • early optimism [Translation. Weaver, W.] • Citing Shannon’s work, he asks: • “If we have useful methods for solving almost any cryptographic problem, may it not be that with proper interpretation we already have useful methods for translation?”

  19. Human Language Technology (HLT) • Popular in the early days and has undergone a modern revival The Present Status of Automatic Translation of Languages (Bar-Hillel, 1951) • “I believe this overestimation is a remnant of the time, seven or eight years ago, when many people thought that the statistical theory of communication would solve many, if not all, of the problems of communication” • Much valuable time spent on gathering statistics

  20. Human Language Technology (HLT) • uneasy relationship between linguistics and statistical analysis Statistical Methods and Linguistics (Abney, 1996) • Chomsky vs. Shannon • Statistics and low (zero) frequency items • Smoothing • No relation between order of approximation and grammaticality • Parameter estimation problem is intractable (for humans) • IBM (17 million parameters)

  21. Human Language Technology (HLT) • recent exciting developments in HLT • precipitated by progress in • computers: stochastic machine learning methods • storage: large amounts of training data • general available of corpora (Linguistic Data Consortium) • University of Arizona Library System is a subscriber • you can borrow many CDROMs of data

  22. Human Language Technology (HLT) • Killer Application?

  23. Natural Language Processing (NLP)Computational Linguistics • Question: • How to process natural languages on a computer • Intersects with: • Computer science (CS) • Mathematics/Statistics • Artificial intelligence (AI) • Linguistic Theory • Psychology: Psycholinguistics • e.g. the human sentence processor

  24. Natural Language Properties which properties are going to be difficult for computers to deal with? • Grammar (Rules for putting words together into sentences) • How many rules are there? • 100, 1000, 10000, more … • Portions learnt or innate • Do we have all the rules written down somewhere? • Lexicon (Dictionary) • How many words do we need to know? • 1000, 10000, 100000 …

  25. Computers vs. Humans • Knowledge of language • Computers are way faster than humans • They kill us at arithmetic and chess • But human beings are so good at language, we often take our ability for granted • Processed without conscious thought • Exhibit complex behavior IBM’s Deep Blue

  26. Examples • Innate Knowledge? • Which report did you file without reading? • (Parasitic gap sentence) • file(x,y) • read(u,v) *the report was filed without reading x = you y = report u = x = you v = y = report and there are no other possible interpretations

  27. Examples • Changes in interpretation • John is too stubborn to talk to • John is too stubborn to talk to Bill talk_to(x,y) (1) x = arbitrary person, y = John (2) x = John, y = Bill

  28. Examples • Ambiguity • Where can I see the bus stop? • stop: verb or part of the noun-noun compound bus stop • Context (Discourse or situation) • Where can I see [the [NN bus stop]]? • Where can I see [[the bus] [V stop]]?

  29. Examples • Ungrammaticality • *Which book did you file the report without reading? • ?*Which book did you file it without reading? • * = ungrammatical • ungrammatical vs. incomprehensible

  30. Example • The human parser has quirks • Ian told the man that he hired a secretary • Ian told the man that he hired a story • Garden-pathing: a temporary ambiguity • tell: multiple syntactic frames for the verb Ian told the agent that he unmasked a secret • Ian told [the man that he hired] [a story] • Ian told [the man] [that he hired a secretary]

  31. Frequently Asked Questions from the Linguistic Society of America (LSA) • http://www.lsadc.org/info/ling-faqs.cfm

  32. LSA (Linguistic Society of America) pamphlet • by Ray Jackendoff • A Linguist’s Perspective on What’s Hard for Computers to Do … • is he right?

  33. If computers are so smart, why can't they use simple English? • Consider, for instance, the four letters read; they can be pronounced as either reed or red. How does the machine know in each case which is the correct pronunciation? Suppose it comes across the following sentences: • (l) The girls will read the paper. (reed) • (2) The girls have read the paper. (red) • We might program the machine to pronounce read as reed if it comes right after will, and red if it comes right after have. But then sentences (3) through (5) would cause trouble. • (3) Will the girls read the paper? (reed) • (4) Have any men of good will read the paper? (red) • (5) Have the executors of the will read the paper? (red) • How can we program the machine to make this come out right?

  34. If computers are so smart, why can't they use simple English? • (6) Have the girls who will be on vacation next week read the paper yet? (red) • (7) Please have the girls read the paper. (reed) • (8) Have the girls read the paper?(red) • Sentence (6) contains both have and will before read, and both of them are auxiliary verbs. But will modifies be, and have modifies read. In order to match up the verbs with their auxiliaries, the machine needs to know that the girls who will be on vacation next week is a separate phrase inside the sentence. • In sentence (7), have is not an auxiliary verb at all, but a main verb that means something like 'cause' or 'bring about'. To get the pronunciation right, the machine would have to be able to recognize the difference between a command like (7) and the very similar question in (8), which requires the pronunciation red.

  35. Berkeley Parser • http://nlp.cs.berkeley.edu/Main.html#Parsing The Berkeley Parser is the most accurate and one of the fastest parsers for a variety of languages.

  36. Berkeley Parser • l) The girls will read the paper. (reed) Verb Tags (Part of Speech Labels) VB - Verb, base form
 VBD - Verb, past tense
 VBG - Verb, gerund or present participle
 VBN - Verb, past participle
 VBP - Verb, non-3rd person singular present
 VBZ - Verb, 3rd person singular present

  37. Berkeley Parser • (2) The girls have read the paper. (red) Verb Tags (Part of Speech Labels) VB - Verb, base form
 VBD - Verb, past tense
 VBG - Verb, gerund or present participle
 VBN - Verb, past participle
 VBP - Verb, non-3rd person singular present
 VBZ - Verb, 3rd person singular present

  38. Berkeley Parser • (3) Will the girls read the paper? (reed) Verb Tags (Part of Speech Labels) VB - Verb, base form
 VBD - Verb, past tense
 VBG - Verb, gerund or present participle
 VBN - Verb, past participle
 VBP - Verb, non-3rd person singular present
 VBZ - Verb, 3rd person singular present

  39. Berkeley Parser • (4) Have any men of good will read the paper? (red) Verb Tags (Part of Speech Labels) VB - Verb, base form
 VBD - Verb, past tense
 VBG - Verb, gerund or present participle
 VBN - Verb, past participle
 VBP - Verb, non-3rd person singular present
 VBZ - Verb, 3rd person singular present

  40. Berkeley Parser • (5) Have the executors of the will read the paper? (red) Verb Tags (Part of Speech Labels) VB - Verb, base form
 VBD - Verb, past tense
 VBG - Verb, gerund or present participle
 VBN - Verb, past participle
 VBP - Verb, non-3rd person singular present
 VBZ - Verb, 3rd person singular present

  41. Part 3 • software already installed here

  42. Your Homework for Today • Download and Install Perl • Active State Perl • Install SWI-Prolog http://www.SWI-Prolog.org/

  43. Perl Resources • http://www.perl.com/ • tutorials etc. • http://perldoc.perl.org/perlintro.html

  44. Perl Resources Google is your friend: many resources out there!

  45. Prolog Resources • Useful Online Tutorials • An introduction to Prolog • (Michel Loiseleur & Nicolas Vigier) • http://invaders.mars-attacks.org/~boklm/prolog/ • Learn Prolog Now! • (Patrick Blackburn, Johan Bos & Kristina Striegnitz) • http://www.coli.uni-saarland.de/~kris/learn-prolog-now/lpnpage.php?pageid=online

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