Transcript Document 7290363

Handout #1
SI 760 / EECS 597 / Ling 702
Language and Information
Winter 2004
Course Information
• Instructor: Dragomir R. Radev
([email protected])
• Office: 3080, West Hall Connector
• Phone: (734) 615-5225
• Office hours: TBA
• Course page:
http://www.si.umich.edu/~radev/LNI-winter2004/
• Class meets on Mondays, 1-4 PM in 412 WH
Readings
• Two introductions to statistical NLP
• Collocations paper
• Joshua Goodman’s language modeling
tutorial
• Documentation for the CMU LM toolkit
N-gram Models
Word Prediction
•
•
•
•
•
Example: “I’d like to make a collect …”
“I have a gub”
“He is trying to fine out”
“Hopefully, all with continue smoothly in my absence”
“They are leaving in about fifteen minuets to go to her
house”
• “I need to notified the bank of [this problem]
• Language model: a statistical model of word sequences
Counting Words
• Brown corpus (1 million words from 500 texts)
• Example: “He stepped out into the hall, was delighted to
encounter a water brother” - how many words?
• Word forms and lemmas. “cat” and “cats” share the same
lemma (also tokens and types)
• Shakespeare’s complete works: 884,647 word tokens and
29,066 word types
• Brown corpus: 61,805 types and 37,851 lemmas
• American Heritage 3rd edition has 200,000 “boldface
forms” (including some multiword phrases)
Unsmoothed N-grams
• First approximation: each word has an equal probability to
follow any other. E.g., with 100,000 words, the probability
of each of them at any given point is .00001
• “the” - 69,971 times in BC, while “rabbit” appears 11
times
• “Just then, the white …”
P(w1,w2,…, wn) = P(w1) P(w2 |w1) P(w3|w1w2) … P(wn |w1w2…wn-1)
A language model
• The sum of probabilities of all strings has to
be 1.
• Bigram and trigram models
Bigram model:
Replace P(wn |w1w2…wn-1) with P(wn|wn-1)
• How do you estimate the probabilities?
Perplexity of a language model
Perp  2
 (1/ N )
 log P ( Si )
• What is the perplexity of guessing a digit if all
digits are equally likely?
• How about a letter?
• How about guessing A with a probability of 1/4, B
with a probability of 1/2 and 10,000 other cases
with a probability of 1/2 total (example modified
from Joshua Goodman).
Perplexity across distributions
• What if the actual distribution is very
different from the expected one?
• Example: all of the 10,000 other cases are
equally likely but P(A) = P(B) = 0.
• Cross-entropy = log (perplexity), measured
in bits
Smoothing techniques
• Imagine the following situation: you are looking at
Web pages and you have to guess how many
different languages they are written in.
• First one is in English, then French, then again
English, then Korean, then Chinese, etc.
Total: 5F, 3E, 1K, 1C
• Can you predict what the next language will be?
• What is a problem with the simplest approach to
this problem?
Smoothing
• Why smoothing?
• How many parameters are there in the
model (given 100,000 possible words)
• What are the unsmoothed (ML) estimates
for unigrams, bigrams, trigrams?
• Linear interpolation (mixture with λi).
• How to estimate λi?
Example
• Consider the problem of estimating bigram
probabilities from a training corpus.
• Probability mass must be 1.
• How to account for unseen events?
Common methods
• Add-1 smoothing (add one to numerator,
add N to denominator)
• Good-Turing smoothing
• Best: Kneser-Ney
Markov Models
• Assumption: we can predict the probability of
some future item on the basis of a short history
• Bigrams: first-level Markov models
• Bigram grammars: as an N-by-N matrix of
probabilities, where N is the size of the vocabulary
that we are modeling.
Relative Frequencies
a
aardvark
aardwolf
aback
…
zoophyte
zucchini
a
X
0
0
0
…
X
X
aardvark
0
0
0
0
…
0
0
aardwolf
0
0
0
0
…
0
0
aback
X
X
X
0
…
X
X
…
…
…
…
…
…
…
…
zoophyte
0
0
0
X
…
0
0
zucchini
0
0
0
X
…
0
0
Language Modeling and
Statistical Machine Translation
The Noisy Channel Model
• Source-channel model of communication
• Parametric probabilistic models of language
and translation
• Training such models
Statistics
• Given f, guess e
e
EF
f
e’
FE
encoder
decoder
e’ = argmax P(e|f) = argmax P(f|e) P(e)
e
e
translation model
language model
Parametric probabilistic models
• Language model (LM)
P(e) = P(e1, e2, …, eL) = P(e1) P(e2|e1) … P(eL|e1 … eL-1)
• Deleted interpolation
P(eL|e1 … eK-1)  P(eL|eL-2, eL-1)
• Translation model (TM)
Alignment: P(f,a|e)
IBM’s EM trained models
1.
2.
3.
4.
5.
Word translation
Local alignment
Fertilities
Class-based alignment
Non-deficient algorithm (avoid overlaps,
overflow)
Bayesian formulas
• argmaxe P(e | f) = ?
• P(e|f) = P(e) * P(f | e) / P(f)
• argmaxe P(e | f) = argmaxe P(e) * P(f | e)
The rest of the slides in this section are based on
“A Statistical MT Tutorial Workbook” by Kevin Knight
N-gram model
• P(e) = ?
• P(how's it going?) = 76,413/1,000,000,000
= 0.000076413
• Bigrams: b(y|x) = count (xy)/count (x)
• P(“I like snakes that are not poisonous”) =
P(“I”|start) * P(“like”|”I”) * …
• Trigrams: b(z|xy) = ??
Smoothing
• b(z | x y) = 0.95 * count (“xyz”) / count (“xy”) +
0.04 * count (“yz”) / count (“z”) +
0.008 * count (“z”) / totalwordsseen +
0.002
Ordering words
(1) a a centre earthquake evacuation forced
has historic Italian of of second southern
strong the the village
(2) met Paul Wendy
Translation models
•
•
•
•
Mary did not slap the green witch.
Mary not slap slap slap the the green witch
Mary no daba una botefada a la verde bruja
Mary no daba una botefada a la bruja verde
Translation models
• Fertility
• Permutation
IBM model 3
•
•
•
•
Fertility
Spurious words (e0)
Pick words
Pick positions
Translation models
•
•
•
•
•
Mary did not slap the green witch.
Mary not slap slap slap the green witch
Mary not slap slap slap NULL the green witch
Mary no daba una botefada a la verde bruja
Mary no daba una botefada a la bruja verde
Parameters
•
•
•
•
N - fertility (x*x)
T - translation (x)
D - position (x)
P
Example
NULL
And
the
program
Le programme
has
a
been
ete
implemen
mis en app
Alignments
The blue house
The blue house
La maison bleue
La maison bleue
• Needed: P(a|f,e)
Markov models
Motivation
• Sequence of random variables that aren’t
independent
• Example: weather reports
Properties
• Limited horizon:
P(Xt+1 = sk|X1,…,Xt) = P(Xt+1 = sk|Xt)
• Time invariant (stationary):
= P(X2=sk|X1)
• Definition: in terms of a transition matrix A
and initial state probabilities .
Example
0.6
h
1.0
0.4
a
p
0.4
0.3
1.0
0.6
0.3
1.0
e
t
i
0.4
start
Visible MM
P(X1,…XT) = P(X1) P(X2|X1) P(X3|X1,X2) … P(XT|X1,…,XT-1)
= P(X1) P(X2|X1) P(X3|X2) … P(XT|XT-1)
T 1
  X 1  a X t X t 1
t 1
P(t, i, p) = P(X1=t) P(X2=i|X1=t) P(X3=p|X2=i)
= 1.0 x 0.3 x 0.6
= 0.18
Hidden MM
0.3
0.7
COLA
ICE TEA
0.5
start
0.5
Hidden MM
• P(Ot=k|Xt=si,Xt+1=sj) = bijk
cola
icetea
lemonade
COLA
0.6
0.1
0.3
ICETEA
0.1
0.7
0.2
Example
• P(lemonade,icetea|COLA) = ?
• P = 0.7 x 0.3 x 0.7 x 0.1 + 0.7 x 0.3 x 0.3 x 0.1
+ 0.3 x 0.3 x 0.5 x 0.7 + 0.3 x 0.3 x 0.5 x 0.7
= 0.084
Hidden MM
• Part of speech tagging, speech recognition, gene
sequencing
• Three tasks:
– A=state transition probabilities, B=symbol emission
probabilities,  = initial state prob.
– Given  = (A,B, ), find P(O|)
– Given O, , what is (X1,…XT+1)
– Given O and a space of all possible , find model that
best describes the observations
Collocations
Collocations
• Idioms
• Free word combinations
• Know a word by the company that it keeps
(Firth)
• Common use
• No general syntactic or semantic rules
• Important for non-native speakers
Examples
Idioms
Collocations
Free-word combinations
To kick the bucket
Dead end
To catch up
To trade actively
Table of contents
Orthogonal projection
To take the bus
The end of the road
To buy a house
Uses
• Disambiguation (e.g, “bank”/”loan”,”river”)
• Translation
• Generation
Properties
•
•
•
•
•
Arbitrariness
Language- and dialect-specific
Common in technical language
Recurrent in context
(see Smadja 83)
Arbitrariness
• Make an effort vs. *make an exertion
• Running commentary vs. *running
discussion
• Commit treason vs. *commit treachery
Cross-lingual properties
• Régler la circulation = direct traffic
• Russian, German, Serbo-Croatian: direct
translation is used
• AE: set the table, make a decision
• BE: lay the table, take a decision
• “semer le désarroi” - “to sow disarray” - “to
wreak havoc”
Types of collocations
• Grammatical: come to, put on; afraid that,
fond of, by accident, witness to
• Semantic (only certain synonyms)
• Flexible: find/discover/notice by chance
Base/collocator pairs
Base
Collocator
Example
Noun
Noun
Verb
Adjective
Verb
verb
adjective
adverb
adverb
preposition
Set the table
Warm greetings
Struggle desperately
Sound asleep
Put on
Extracting collocations
• Mutual information
I (x;y) = log2
P(x,y)
P(x)P(y)
• What if I(x;y) = 0?
• What if I(x;y) < 0?
Yule’s coefficient
A - frequency of lemma pairs involving both
Li and Lj
B - frequency of pairs involving Li only
C - frequency of pairs involving Lk only
D - frequency of pairs involving neither
YUL =
AD - BC
AD + BC
-1  YUL  1
Specific mutual information
• Used in extracting bilingual collocations
p (e,f)
I (e,f) =
p(e) p(f)
• p(e,f) - probability of finding both e and f in
aligned sentences
• p(e), p(f) - probabilities of finding the word in one
of the languages
Example from the Hansard
corpus (Brown, Lai, and Mercer)
French word Mutual information
sein
5.63
bureau
5.63
trudeau
premier
5.34
5.25
résidence
intention
no
5.12
4.57
4.53
session
4.34
Flexible and rigid collocations
• Example (from Smadja): “free” and “trade”
Total p-5 p-4 p-3 p-2
8031
7
6
13
5
p-1
7918
p+1 p+2 p+3 p+4 p+5
0
12
20
26
24
Xtract (Smadja)
• The Dow Jones Industrial Average
• The NYSE’s composite index of all its
listed common stocks fell *NUMBER* to
*NUMBER*
Translating Collocations
• Brush up a lesson, repasser une leçon
• Bring about/осуществлять
• Hansards: late spring: fin du printemps,
Atlantic Canada Opportunities Agency,
Agence de promotion économique du
Canada atlantique