Foundations of Statistical Natural Language Processing

Dictionary-Based

disambiguation-based on tran
(3/

„ For disambiguation (for e

„ Step1

„ Count the number of times
of interest occur with transl
language corpus

„ Setp2

„ Compare the counts of the t

„ Step3

„ Choose the sense that has t
corresponding sense

d Disambiguation

nslations in a second-language
/3)

example {interest, show} )

that translations of the two senses
lations of show in the second

two different senses
the higher counts as a

Dictionary-Based

one sense per discourse, o
(1/2

„ Most dictionary-based a
occurrence separately.

„ There are constraints be
occurrences that can be
disambiguation.

„ One sense per discourse

„ The sense of a target wo
any given document.

„ One sense per collocatio

„ Nearby words provide st
the sense of a target wo
context)

Disambiguation

one sense per collocation
2)
algorithms process each

etween different
e exploited for

e

ord is highly consistent within

on

trong and consistent clues to
ord. (word sense depends on

Dictionary-Based

one sense per discourse,
(2/

„ The first constraint is es

„ The material to be disam
small documents

„ Or can be divided into sh

„ For example

„ Discourse initial label co

„ D1 living th

„ D1 living cla

„ D1 ? Alt

d Disambiguation

one sense per collocation
/2)
specially useable when

mbiguated is a collection of

hort discourses

ontext

he existence of plant and animal life
assified as either plant of animal

though bacterial and plant cells are…

Unsupervised Disa

„ ( Schutze,1998 )

„ Disambiguate word sens
supporting tools such as
in the absence of labeled

„ Simply cluster the contex
into a number of groups
these groups without lab

„ The probabilistic model i
as the one used for supe
P(vj | sk) are estimated u

ambiguation (1/3)

ses without having resource to
dictionaries and thesauri and

d text.
xts of an ambiguous word
and discriminate between
beling them.
is the same Bayesian model
ervised classification, but the
using the EM algorithm.

Unsupervised Disa

„ EM algorithm

„ Initialize p(v j | sk ) Æ r
„ Compute likelihood l(C

IK

l(C | µ) = log∏ ∑ p(ci | sk )
i=1 k =1

„ While l(C | µ) is improvi

„ E step : hi,k = p(ci | s

ΣK p(ci
k =1
„ M step : Re-estimate
Σ h{ci:v j∈ci } i
p(v j | sk ) = Σ ΣK

k =1 {ci:v j∈ci }

ambiguation (2/3)

random K

∑C | µ), and P(ci ) = P(ci | sk )P(sk )
k =1
IK

∑ ∑) p(sk ) = log p(ci | sk ) p(sk )
i=1 k =1

∏ing repeat: p(ci | sk ) = p(v j | sk )
s ) v j∈ci
k

i | sk )

i,k p(sk ) = ΣiI=1hi,k
ΣkK=1ΣiI=1hi,k
h} i,k

Unsupervised Disa

KK

22

1 1

Context 1 Context 2

ambiguation (3/3)

K

2
1

Context 3

„The

e End