Finite-State Methods In Natural Language Processing (600.405)
		   Jason Eisner (jason@cs.jhu.edu)
		First-Day Questionnaire - Nov. 7, 2000

Name: 

Email: 

Status & year: 

Favorite candy bar: 

Hobbies:

Other identifying features (or self-portrait): 


Are you in this class for credit, for audit, or in secret?

What JHU computer networks do you have accounts on?
If you prefer using some networks to others, please
also rank them on the dashed lines.
  [ ]  ____ CS research network (peregrine, condor, ...)
  [ ]  ____ CS undergrad network (hops, malt, ...)
  [ ]  ____ CS NLP lab network (bigram, phrase, ...)
  [ ]  ____ CLSP network (speak, dc02, ...)

  [ ]  ____ Other ____________________________________


On a scale of 1-4, how much do you hope to get out of this
course in each of the following three areas?

		      1           2            3                4
		  (exposure)  (comfort) (full or deep    (preparation to
                                         understanding)   extend the work)
  Theory
  Applications
  Software tools

On the same scale of 0 to 4, how much do you know (and remember) about
each of the following topics?  (Don't worry, this info won't be used
against you - I'm just trying to find out what the class as a whole
knows! :-)

___ determinization of finite-state automata
___ minimization of finite-state automata
___ epsilon-closure (epsilon-removal) in finite-state automata
___ pumping lemma
___ regular expressions (regexps)
___ extensions to regexps (e.g., in Perl): _______________________________
___ push-down automata
___ context-free parsing
___ undecidable problems for CFGs or PDAs
___ Turing machines
___ closure of a set under given operations
___ group theory
___ ring theory

(over, please)

___ rings of polynomials
___ power series (e.g., Taylor series)
___ convergence to a limit
___ measure theory
___ topology (e.g., metric spaces)
___ derivational phonology
___ optimality theory
___ morphology
___ templatic morphology
___ two-level morphology
___ part-of-speech tagging
___ conditional probability
___ Bayes' theorem
___ language modeling
___ simulated annealing
___ gradient descent
___ other optimization techniques: ________________________________________
___ forward-backward algorithm
___ inside-outside algorithm
___ k-means clustering algorithm
___ expectation-maximization (EM)
___ other unsupervised learning techniques: _______________________________
___ decision trees
___ decision lists
___ transformation-based learning ("Brill rules")
___ simple Prolog programming     


What are your main research interests?




Have you ever chosen on your own to use finite-state methods?  What for?




Any other background relevant to this course?




Anything else you want to say?



Write a regular expression that accepts only binary numbers that
are divisible by 4.


A binary number is divisible by 3 iff the number of 1's in even
positions = the number of 1's in odd positions (mod 3).  For example,
1010111 = 87 = 29*3 has four 1's in even positions and one 1 in an odd
position.  Draw a finite-state machine that accepts only binary
numbers that are divisible by 3.