1. A Comparison of Unsupervised
Bilingual Term Extraction
Methods Using Phrase-Tables
Masamichi Ideue†
Kazuhide Yamamoto
Masao Utiyama
Eiichiro Sumita
‡
Nagaoka University of
Technology, Japan
†
National Institutre of
Information
and Communications
Technology
†
‡
‡

2. Background
• Automatic bilingual term extraction
• Helpful for human translators
• Applicable to other NLP tasks
Develop unsupervised methods
for extracting bilingual terms
from a phrase-table, and compare
them.
Goal
1

3. Related works
• Using parallel corpus
• Using existing bilingual dictionary
Tonoike et al. (2006) translated the number of word in
each source language term using the bilingual dictionary
and combined these translations to form term candidates.
2
Itagaki et al. (2007) proposed a supervised method for
extracting bilingual terms from the phrase-table built from
a parallel corpus.
We usually do not have annotated data for
training supervised methods nor bilingual
dictionaries specific to the documents under
translation.

5. 3

6. Statistical measures 4
Three statistical scores are used to eliminate
the wrong pairs, respectively.
: Significance of the candidates
based on Fisher’s exact test.
ScoreF
: Strength of the alignment
between words of the candidates.
ScoreL
: Termhood of the candidate
based on C-value.
ScoreC

7. Bilingual term counting and
combination of measures
• Combination of scores
5
,
2 , 2 , ,
Score ( )
(Score ( )) (Score ( )) (Score ( ))
3
FLC J E
F J E L J E C J E
T
R T R T R T
=
+ +
• Two methods for counting the number of
occurrences of term T
Method 1 : Counting without regarding where T occurs
Method 2 : Counting T only when it occurs alone, i.e.,
we do not count the number of occurrences of term T
when it occurs as a substring of a longer term.

8. Experiments
100 bilingual term candidates that were randomly
selected from the top 1,000 candidates were manually
evaluated for each score.
A : correct
A' : correct depending on contexts
B : partly correct
C : incorrect
Evaluation criterion
6
• 22,543 bilingual term candidates were extracted
from the Phrase-table.
• Training corpus : Japanese-English parallel corpus,
consisting of about 60,000 pairs, related to apparel
products.

9. Translation accuracy
A A' B C
F1 43 25 24 8
L1 77 5 18 0
C 78 6 14 2
F2 71 18 8 3
L2 79 4 17 0
FLC 87 2 11 0
, , , and
can filter the extracted noisy
bilingual term.
7
2ScoreF
ScoreFLC
ScoreC2ScoreL

10. Characteristics of extracted
bilingual terms
occurrences words
F2 Many Few
L2 Many Many
C Few Many
FLC Few Many
• Each measure extracts different bilingual term
candidates.
• The characteristic of indicated a
tendency similar to . From this, the
’s residual noise was filtered by
and .
8
ScoreFLC
2ScoreF 2ScoreL
ScoreC
ScoreC

11. Conclusion
We compared three statistical measures for
extracting bilingual terms from the phrase-
table built from a parallel corpus.
Each method differs in the number of
words and the occurrences of bilingual
terms.
The combination of these measures
ranks valid bilingual terms highly.
9

12. Fisher's exact test
: Significance of the candidates
• Fisher’s exact test has been used by Johnson et
al. (2007) to select valid phrase pairs from the
phrase-table for statistical machine translation.
We use the statistic of Fisher’s exact test as Score_F
to measure the validity of each bilingual term
candidate. If Score_F of a bilingual term candidate
is high, the candidate has the validity.
ScoreF

13. Score_F
C(J,E) C(J)-C(J,E) C(J)
C(E)-C(J,E) N-C(J)-C(E)+C(J,E) N-C(J)
C(E) N-C(E) N
N : All parallel sentences
C(J) : Japanese sentences containing J
C(E): English sentences containing E
C(J,E) : The number of parallel
sentences containing J and E
• P_h (C(J, E)) is the probability of observing the
contingency table under the null hypothesis of J and E
being independent of each other.

14. Log-likelihood Ratio
• Tonoike et al. (2007) said alignments of
a component of the term is useful for
automatic bilingual term extraction.
: Strength of the alignmentScoreL
Using the word alignments of each
candidate term to measure the validity of
the candidates.

15. Alignment information
We used the alignment information produced by
Moses (Koehn et al., 2007).
Alignments
in the parallel
sentences
Alignments in ,J ET

16. C-Value
If the term candidates of both language are
highly ranked in C-value ranking, the
bilingual term candidate has validity.
: Termhood of the candidate
color denim pants (C-Value = 6.34)
color denim (2.0) denim pants (60.33)
ScoreC
The C-value (Frantzi et al., 1996) has been
used to measure the stability of nested multi-
word term candidates.

17. Bilingual term counting and
combination of measures
Our experiments show that the counting method is better
than normal counting and characteristics of each measure
are different. Therefore, we combine them.
• Combination of measures
• Two methods for counting the
number of occurrences of term T
Method 1 : Counting without regarding where T occurs
Method 2 : Counting T only when it occurs alone, i.e.,
we do not count the number of occurrences of term T
when it occurs as a substring of a longer term.
2 , 2 , ,
,
(Score ( )) (Score ( )) (Score ( ))
Score ( )
3
F J E L J E C J E
FLC J E
R T R T R T
T
+ +
=

18. Examples of the extracted bilingual term
daiya diamond
daun
jaketto
down
jacket
kitake
nagame
long
length
wanpi-
su
one-
piece
kata
osi
embosse
d leather
ga-ze
sozai
gauze
material
siagari finish
kisetu
kan
seasonal
look
kobana
gara
floral
pattern
pointo accent
iro
zukai
coloring
pasu
ke-su
card
case
B
uesuto
bubun
(waist
part)
waist
konbou
sozai
(blend
material)
blend
iro oti
(faced
color)
faded
look
C
sodeguti
(cuff) hem
siruetto
bodi-
(body
silhouette)
item
features
A'
F2 L2 C
A