1. 438/538 Computational Linguistics Sandiway Fong Lecture 1: 8/22

2. Part 1 Administrivia

3. Where –BIO W 212 When –TR 12:30–1:45PM No Class –Thursday September 14th –Thursday September 28th –Thursday November 23rd (Thanksgiving) Office Hours –catch me after class, or –by appointment –Location: Douglass 311

4. Administrivia Map –Office (Douglass) –Classroom (FCS)

5. Administrivia Email –sandiway@email.arizona.edusandiway@email.arizona.edu Homepage –http://dingo.sbs.arizona.edu/~sandiwayhttp://dingo.sbs.arizona.edu/~sandiway Lecture slides –available on homepage after each class –in both PowerPoint (.ppt) and Adobe PDF formats animation: in powerpoint –last year’s slides are available (new material for this year will be rotated in)

6. Administrivia Reference Textbook Speech and Language Processing, Jurafsky & Martin, Prentice-Hall 2000 –21 chapters (900 pages) –Concepts, algorithms, heuristics –Sound/speech side N. Warner Speech Tech LING 578 (this semester) Y. Lin Statistical NLP LING 539 (Spring 2007) –Intersection with research areas Parsing and Linguistic Theory (Sentence Processing) Computational Morphology Machine Translation, WordNet

7. Administrivia Course Objectives –Theoretical Introduction to a broad selection of natural language processing techniques Survey course Relevance to linguistic theory –Practical Acquire some expertise –Parsing algorithms –Write grammars and machines –Build a toy machine translation system

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

9. Administrivia Homeworks and Grading –6~8 homeworks no final or mid-terms mix of theoretical and practical exercises there will be mandatory and extra credit questions –extra credit questions matter: »make up points lost on other questions in the homework »may bump you up a grade at the end of the semester in borderline situations some simple programming is involved (no prerequisite) use of a spreadsheet (Excel) for numerical calculation

10. Administrivia Homeworks and Grading –Homeworks will be presented/explained in class (good chance to ask questions) –Please attempt homeworks early (then you can ask questions before the deadline) –Unless otherwise specified, 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 –Look for acknowledgement email from me

11. Administrivia Homework Ethics –you may discuss homework with your classmates –however, you must do the work and write them up independently –sources must be acknowledged, e.g. if you borrow program code off the internet discovered cheaters will be sanctioned Late Policy –all homework is mandatory you can’t get an A skipping a homework some homeworks may depend on earlier homeworks –deductions if late –if you know you are going to be late, notify me ahead of time e.g. upcoming emergencies

12. Administrivia 438 vs. 538 538 = 438 + 1 classroom presentation of a selected chapter + 438 extra credit homework questions are obligatory

13. Administrivia There is a laptop being passed around Fill out spreadsheet entry –Name –Email –Year/Major –438 or 538 –Relevant background

14. Administrivia Class demographics (8/20 classlist)

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 Killer Apps: –Language comprehension tasks and Machine Translation (MT) Reference –Readings in Machine Translation –Eds. Nirenburg, S. et al. MIT Press 2003. –(Part 1: Historical Perspective)

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 perhaps no longer a bottleneck

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 –recent improvements in stochastic models from incorporating linguistic knowledge –(Hovy, MT Summit 2003)

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) x = you y = report u = x = you v = y = report and there are no other possible interpretations *the report was filed without reading

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? –* = ungrammatical relative –ungrammatical vs. incomprehensible

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

31. Examples More subtle differences The reporter who the senator attacked admitted the error The reporter who attacked the senator admitted the error –Processing time differences Subject vs. object relative clauses –Q: Do we want to mimic the human parser completely?

32. Frequently Asked Questions from the Linguistic Society of America (LSA) http://www.lsadc.or g/info/ling-faqs.cfm

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

34. 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?

35. 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.

36. Next time … We will begin by introducing you to a programming language you will become familiar with –Two introductory lectures –Name: PROLOG (Programming in Logic) –Variant: SWI-PROLOG (free software from University of Amsterdam) Download: http://www.swi-prolog.org/http://www.swi-prolog.org/ Install it on your PC or Mac –Based on mathematical logic logic and inference are useful tools –Contains built-in grammar rules programming language was originally designed for NLP

37. Prolog Resources Some background in programming? Useful Online Tutorials –An introduction to Prolog (Michel Loiseleur & Nicolas Vigier) http://invaders.mars- attacks.org/~boklm/prolog/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=onlinehttp://www.coli.uni- saarland.de/~kris/learn-prolog- now/lpnpage.php?pageid=online

38. Prolog Resources No background at all? Audit –LING 388 Computers and Language (also taught by me) first couple of weeks introduces Prolog at a more gentle pace uses lab classes for practice Lectures TR 3:30–4:45pm –Harvill 313 Hands-on Lab Class: this Thursday –Social Sciences 224