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LING/C SC/PSYC 438/538 Computational Linguistics

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Title: LING/C SC/PSYC 438/538 Computational Linguistics


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

2
Part 1
  • Administrivia

3
Administrivia
  • Where
  • S SCI 224
  • When
  • TR 1230145PM (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

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

5
Administrivia
  • Email
  • sandiway_at_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
Administrivia
  • 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

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 Masters 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
Administrivia
  • Homeworks
  • Homeworks will be presented/explained in class
  • (good chance to ask 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

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
Class Questionnaire
  • Ill pass my laptop around ...
  • Use PhotoBooth
  • Fill in Excel spreadsheet
  • Name
  • PhotoBooth
  • Email
  • Major
  • Any programming expertise?
  • Have a laptop?
  • Knowledge of Linguistics?

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 Shannons 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

IBMs 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 Linguists Perspective on Whats 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.htmlParsing

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?pageidonline
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