A Pattern Matching Method for Finding Noun and Proper Noun 
Translations from Noisy Parallel Corpora 
Pascale Fung 
Computer Science Department 
Columbia University 
New York, NY 10027 
pascale©cs, columbia, edu 
Abstract 
We present a pattern matching method for 
compiling a bilingual lexicon of nouns and 
proper nouns from unaligned, noisy paral- 
lel texts of Asian/Indo-European language 
pairs. Tagging information of one lan- 
guage is used. Word frequency and posi- 
tion information for high and low frequency 
words are represented in two different vec- 
tor forms for pattern matching. New an- 
chor point finding and noise elimination 
techniques are introduced. We obtained 
a 73.1% precision. We also show how the 
results can be used in the compilation of 
domain-specific noun phrases. 
1 Bilingual lexicon compilation 
without sentence alignment 
Automatically compiling a bilingual lexicon of nouns 
and proper nouns can contribute significantly to 
breaking the bottleneck in machine translation and 
machine-aided translation systems. Domain-specific 
terms are hard to translate because they often do 
not appear in dictionaries. Since most of these terms 
are nouns, proper nouns or noun phrases, compiling 
a bilingual lexicon of these word groups is an impor- 
tant first step. 
We have been studying robust lexicon compilation 
methods which do not rely on sentence alignment. 
Existing lexicon compilation methods (Kupiec 1993; 
Smadja & McKeown 1994; Kumano & Hirakawa 
1994; Dagan et al. 1993; Wu & Xia 1994) all attempt 
to extract pairs of words or compounds that are 
translations of each other from previously sentence- 
aligned, parallel texts. However, sentence align- 
ment (Brown et al. 1991; Kay & RSscheisen 1993; 
Gale & Church 1993; Church 1993; Chen 1993; 
Wu 1994) is not always practical when corpora have 
unclear sentence boundaries or with noisy text seg- 
ments present in only one language. 
Our proposed algorithm for bilingual lexicon ac- 
quisition bootstraps off of corpus alignment proce- 
dures we developed earlier (Fung & Church 1994; 
Fung & McKeown 1994). Those procedures at- 
tempted to align texts by finding matching word 
pairs and have demonstrated their effectiveness for 
Chinese/English and Japanese/English. The main 
focus then was accurate alignment, but the proce- 
dure produced a small number of word translations 
as a by-product. In contrast, our new algorithm per- 
forms a minimal alignment, to facilitate compiling a 
much larger bilingual lexicon. 
The paradigm for Fung ~: Church (1994); Fung 
& McKeown (1994) is based on two main steps - 
find a small bilingual primary lexicon, use the text 
segments which contain some of the word pairs in 
the lexicon as anchor points for alignment, align the 
text, and compute a better secondary lexicon from 
these partially aligned texts. This paradigm can be 
seen as analogous to the Estimation-Maximization 
step in Brown el al. (1991); Dagan el al. (1993); Wu 
& Xia (1994). 
For a noisy corpus without sentence boundaries, 
the primary lexicon accuracy depends on the robust- 
ness of the algorithm for finding word translations 
given no a priori information. The reliability of the 
anchor points will determine the accuracy of the sec- 
ondary lexicon. We also want an algorithm that 
bypasses a long, tedious sentence or text alignment 
step. 
2 Algorithm overview 
We treat the bilingual lexicon compilation problem 
as a pattern matching problem - each word shares 
some common features with its counterpart in the 
translated text. We try to find the best repre- 
sentations of these features and the best ways to 
match them. We ran the algorithm on a small Chi- 
nese/English parallel corpus of approximately 5760 
unique English words. 
The outline of the algorithm is as follows: 
1. Tag the English half of the parallel text. 
In the first stage of the algorithm, only En- 
glish words which are tagged as nouns or proper 
nouns are used to match words in the Chinese 
text. 
236 
2. Compute the positional difference vector 
of each word. Each of these nouns or proper 
nouns is converted from their positions in the 
text into a vector. 
3. Match pairs of positional difference vec- 
tors~ giving scores. All vectors from English 
and Chinese are matched against each other by 
Dynamic Time Warping (DTW). 
4. Select a primary lexicon using the scores. 
A threshold is applied to the DTW score of each 
pair, selecting the most correlated pairs as the 
first bilingual lexicon. 
5. Find anchor points using the primary lex- 
icon. The algorithm reconstructs the DTW 
paths of these positional vector pairs, giving us 
a set of word position points which are filtered 
to yield anchor points. These anchor points are 
used for compiling a secondary lexicon. 
6. Compute a position binary vector for 
each word using the anchor points. The re- 
maining nouns and proper nouns in English and 
all words in Chinese are represented in a non- 
linear segment binary vector form from their po- 
sitions in the text. 
7. Match binary vectors to yield a secondary 
lexicon. These vectors are matched against 
each other by mutual information. A confidence 
score is used to threshold these pairs. We ob- 
tain the secondary bilingual lexicon from this 
stage. 
In Section 3, we describe the first four stages in 
our algorithm, cumulating in a primary lexicon. Sec- 
tion 4 describes the next anchor point finding stage. 
Section 5 contains the procedure for compiling the 
secondary lexicon. 
3 Finding high frequency bilingual 
word pairs 
When the sentence alignments for the corpus are un- 
known, standard techniques for extracting bilingual 
lexicons cannot apply. To make matters worse, the 
corpus might contain chunks of texts which appear 
in one language but not in its translation 1, suggest- 
ing a discontinuous mapping between some parallel 
texts. 
We have previously shown that using a vector rep- 
resentation of the frequency and positional informa- 
tion of a high frequency word was an effective way to 
match it to its translation (Fung & McKeown 1994). 
Dynamic Time Warping, a pattern recognition tech- 
nique, was proposed as a good way to match these 
1This was found to be the case in the Japanese trans- 
lation of the AWK manual (Church et al. 1993). The 
Japanese AWK was also found to contain different pro- 
gramming examples from the English version. 
vectors. In our new algorithm, we use a similar po- 
sitional difference vector representation and DTW 
matching techniques. However, we improve on the 
matching efficiency by installing tagging and statis- 
tical filters. In addition, we not only obtain a score 
from the DTW matching between pairs of words, 
but we also reconstruct the DTW paths to get the 
points of the best paths as anchor points for use in 
later stages. 
3.1 Tagging to identify nouns 
Since the positional difference vector representation 
relies on the fact that words which are similar in 
meaning appear fairly consistently in a parallel text, 
this representation is best for nouns or proper nouns 
because these are the kind of words which have con- 
sistent translations over the entire text. 
As ultimately we will be interested in finding 
domain-specific terms, we can concentrate our ef- 
fort on those words which are nouns or proper nouns 
first. For this purpose, we tagged the English part of 
the corpus by a modified POS tagger, and apply our 
algorithm to find the translations for words which 
are tagged as nouns, plural nouns or proper nouns 
only. This produced a more useful list of lexicon and 
again improved the speed of our program. 
3.2 Positional difference vectors 
According to our previous findings (Fung& McK- 
eown 1994), a word and its translated counterpart 
usually have some correspondence in their frequency 
and positions although this correspondence might 
not be linear. Given the position vector of a word 
p\[i\] where the values of this vector are the positions 
at which this word occurs in the corpus, one can 
compute a positional difference vector V\[i- 1\] where 
Vii- 1\] = p\[i\]- p\[i- 1\]. dim(V) is the dimension 
of the vector which corresponds to the occurrence 
count of the word. 
For example, if positional difference vectors for the 
word Governor and its translation in Chinese .~ 
are plotted against their positions in the text, they 
give characteristic signals such as shown in Figure 1. 
The two vectors have different dimensions because 
they occur with different frequencies. Note that the 
two signals are shifted and warped versions of each 
other with some minor noise. 
3.3 Matching positional difference vectors 
The positional vectors have different lengths which 
complicates the matching process. Dynamic Time 
Warping was found to be a good way to match word 
vectors of shifted or warped forms (Fung & McK- 
eown 1994). However, our previous algorithm only 
used the DTW score for finding the most correlated 
word pairs. Our new algorithm takes it one step fur- 
ther by backtracking to reconstruct the DTW paths 
and then automatically choosing the best points on 
these DTW paths as anchor points. 
237 
16G00 
140Q0 
12000 
10000 
800O 
6OOO 
4O0O 
200O 
0 
50 1OO ~ 150 200 250 word pos~ M 
text 
"govemor.ch.vec.diff" -- 
T4000 
10000 
300 
80QO 
20O0 
50 100 150 200 word positiorl in text 
• govem~.en.vec.diff" -- 
250 
Figure 1: Positional difference signals showing similarity between Governor in English and Chinese 
For a given pair of vectors V1, V2, we attempt 
to discover which point in V1 corresponds to which 
point in V2 . If the two were not scaled, then po- 
sition i in V1 would correspond to position j in V2 
where j/i is a constant. If we plot V1 against V2, 
we can get a diagonal line with slope j/i. If they 
occurred the same number of times, then every po- 
sition i in V1 would correspond to one and only one 
position j in V2. For non-identical vectors, DTW 
traces the correspondences between all points in V1 
and V2 (with no penalty for deletions or insertions). 
Our DTW algorithm with path reconstruction is as 
follows: 
• Initialization 
where 
~oz(1,1) = ((1,1) 
¢pl(i, 1) = ¢(i, 1) + ~o(i - 1, 1\]) 
toz(1,j) = ff(1,j)+~o(1,j-a) 
9~(a, b) = minimum cost of moving 
from a to b 
((c,d) = IVl\[c\]- V2\[aq\[ 
for i = 1,2,...,N 
j = 1,2,...,M 
g = dim(V1) 
M = dim(V2) 
• Recursion 
~on+l (i, m) min \[~(l, m) + ~o.(i,/)\] 1</<3 
for n 
and m 
= argmin\[~(/, m) + ~n(i, 1)\] 
1<1<3 
= 1,2,...,N-2 
= 1,2,...,M 
• Termination 
~ON(i, j) = min ~oN-1 (i,/)\] 1</<3\[I(1 , rt2) + 
(N(j) = argmin\[~(l,m) + ~oN-x(i,j)\] 
1_</_<3 
• Path reconstruction 
In our algorithm, we reconstruct the DTW path 
and obtain the points on the path for later use. 
The DTW path for Governor/~d~,~ is as shown 
in Figure 2. 
optimal path - (i, il,i2,... ,im-2,j) 
where in = ~n+l(in+l), 
n -- N- 1,N- 2,... ,1 
with iN = j 
We thresholded the bilingual word pairs obtained 
from above stages in the algorithm and stored the 
more reliable pairs as our primary bilingual lexicon. 
3.4 Statistical filters 
If we have to exhaustively match all nouns and 
proper nouns against all Chinese words, the match- 
ing will be very expensive since it involves comput- 
ing all possible paths between two vectors, and then 
backtracking to find the optimal path, and doing this 
for all English/Chinese word pairs in the texts. The 
complexity of DTW is @(NM) and the complexity 
of the matching is O(IJNM) where I is the number 
of nouns and proper nouns in the English text, J is 
the number of unique words in the Chinese text, N 
is the occurrence count of one English word and M 
the occurrence count of one Chinese word. 
We previously used some frequency difference con- 
straints and starting point constraints (Fung & 
McKeown 1994). Those constraints limited the 
238 
W 
500000 
1001~ 
path f 
| i i i i 
100otm ~ 300~o 40o00o 50000o 
Figure 2: Dynamic Time Warping path for Governor in English and Chinese 
number of the pairs of vectors to be compared by 
DTW. For example, low frequency words are not 
considered since their positional difference vectors 
would not contain much information. We also ap- 
ply these constraints in our experiments. However, 
there is still many pairs of words left to be compared. 
To improve the computation speed, we constrain 
the vector pairs further by looking at the Euclidean 
distance g of their means and standard deviations: 
E = ~/iml - m2) 2 + (~1 - ~2)~ 
If their Euclidean distance is higher than a cer- 
tain threshold, we filter the pair out and do not use 
DTW matching on them. This process eliminated 
most word pairs. Note that this Euclidean distance 
function helps to filter out word pairs which are very 
different from each other, but it is not discriminative 
enough to pick out the best translation of a word. 
So for word pairs whose Euclidean distance is below 
the threshold, we still need to use DTW matching 
to find the best translation. However, this Euclidean 
distance filtering greatly improved the speed of this 
stage of bilingual lexicon compilation. 
4 Finding anchor points and 
eliminating noise 
Since the primary lexicon after thresholding is rela- 
tively small, we would like to compute a secondary 
lexicon including some words which were not found 
by DTW. At stage 5 of our algorithm, we try to 
find anchor points on the DTW paths which divide 
the texts into multiple aligned segments for compil- 
ing the secondary lexicon. We believe these anchor 
points are more reliable than those obtained by trac- 
ing all the words in the texts. 
For every word pair from this lexicon, we had ob- 
tained a DTW score and a DTW path. If we plot the 
points on the DTW paths of all word pairs from the 
lexicon, we get a graph as in the left hand side of Fig- 
ure 3. Each point (i, j) on this graph is on the DTW 
path(vl, v2) where vl is from English words in the 
lexicon and v2 is from the Chinese words in the lexi- 
con. The union effect of all these DTW paths shows 
a salient line approximating the diagonal. This line 
can be thought of the text alignment path. Its de- 
parture from the diagonal illustrates that the texts 
of this corpus are not identical nor linearly aligned. 
Since the lexicon we computed was not perfect, 
we get some noise in this graph. Previous align- 
ment methods we used such as Church (1993); Fung 
& Church (1994); Fung & McKeown (1994) would 
bin the anchor points into continuous blocks for a 
rough alignment. This would have a smoothing ef- 
fect. However, we later found that these blocks of 
anchor points are not precise enough for our Chi- 
nese/English corpus. We found that it is more ad- 
vantageous to increase the overall reliability of an- 
chor points by keeping the highly reliable points and 
discarding the rest. 
From all the points on the union of the DTW 
paths, we filter out the points by the following con- 
ditions: If the point (i, j) satisfies 
(slope constraint) j/i > 600 * N\[0\] 
(window size constraint) i >= 25 -t- iprevious (continuity constraint) j 
>= Jpreviou, 
(offset constraini) j -- jprevious > 500 
then the point (i, j) is noise and is discarded. 
After filtering, we get points such as shown in the 
right hand side of Figure 3. There are 388 highly re- 
liable anchor points. They divide the texts into 388 
segments. The total length of the texts is around 
100000, so each segment has an average window size 
of 257 words which is considerably longer than a sen- 
tence length; thus this is a much rougher alignment 
than sentence alignment, but nonetheless we still get 
a bilingual lexicon out of it. 
239 
IO00(X) 
90OO0 
8O000 
70000 
6O00O 
5O000 
40000 
3O00O 
2C000 
10OOO 
0 
, , , , v 
~ece "a I.dlw.pos" • 
~o e 
• $ ,t ,,~J"O '~*¢ 
o * %•• ° *,~* r'* * 
4' *~o ,~4!Pt s 
°--•'°" ~ " ~.4R " ¢ . oe . .5,,,=:~. ~-¢ • , 
° ".,~" t .°e . 
20000 40000 600(\]0 80000 100000 120000 
100000 v I 
90ooo i- 
80000 k 
7o~o 
o 6OOO0 F 
500OO F ~¢e ee~o 
3OOOO F 
1o000 F • .'f, 
0-- ~ = i i 
0 10000 20000 30000 40000 50000 
d' ; v 
"finered.dtw,pos" e¢ • 
,7. 
I t l I 
66000 70000 80000 90000 100000 
Figure 3: DTW path reconstruction output and the anchor points obtained after filtering 
The constants in the above conditions are cho- 
sen roughly in proportion to the corpus size so that 
the filtered picture looks close to a clean, diagonal 
line. This ensures that our development stage is still 
unsupervised. We would like to emphasize that if 
they were chosen by looking at the lexicon output 
as would be in a supervised training scenario, then 
one should evaluate the output on an independent 
test corpus. 
Note that if one chunk of noisy data appeared in 
text1 but not in text2, this part would be segmented 
between two anchor points (i, j) and (u, v). We know 
point i is matched to point j, and point u to point 
v, the texts between these two points are matched 
but we do not make any assumption about how this 
segment of texts are matched. In the extreme case 
where i -- u, we know that the text between j and 
v is noise. We have at this point a segment-aligned 
parallel corpus with noise elimination. 
5 Finding low frequency bilingual 
word pairs 
Many nouns and proper nouns were not translated in 
the previous stages of our algorithm. They were not 
in the first lexicon because their frequencies were too 
low to be well represented by positional difference 
vectors. 
5.1 Non-linear segment binary vectors 
In stage 6, we represent the positional and frequency 
information of low frequency words by a binary vec- 
tor for fast matching. 
The 388 anchor points (95,10), (139,131),..., 
(98809, 93251) divide the two texts into 388 non- 
linear segments. Textl is segmented by the points 
(95,139,..., 98586, 98809) and text2 is segmented 
by the points (10,131,..., 90957, 93251). 
For the nouns we are interested in finding the 
translations for, we again look at the position 
vectors. For example, the word prosperity oc- 
curred seven times in the English text. Its posi- 
tion vector is (2178, 5322,... ,86521,95341) . We 
convert this position vector into a binary vector 
V1 of 388 dimensions where VI\[i\] = 1 if pros- 
perity occured within the ith segment, VI\[i\] -- 
0 otherwise. For prosperity, VI\[i\] -- 1 where 
i = 20, 27, 41, 47,193,321,360. The Chinese trans- 
lation for prosperity is ~!. Its position vec- 
tor is (1955,5050,... ,88048). Its binary vector is 
V2\[i\] = 1 where i = 14, 29, 41, 47,193,275,321,360. 
We can see that these two vectors share five segments 
in common. 
We compute the segment vector for all English 
nouns and proper nouns not found in the first lex- 
icon and whose frequency is above two. Words oc- 
curring only once are extremely hard to translate 
although our algorithm was able to find some pairs 
which occurred only once. 
5.2 "Binary vector correlation measure 
To match these binary vectors V1 with their coun- 
terparts in Chinese V2, we use a mutual information 
score m. 
Pr(V1, V2) 
m = log2 Pr(Vl) Pr(V2) 
freq(Vl\[i\] = 1) Pr(V1) -- 
L 
freq(V2\[i\] = 1) Pr(V2) = 
L 
freq(Vl\[i\] -- V2\[i\] - 1) Pr(VI,V2) = 
L 
where L = dim(V1) = dim(V2) 
240 
If prosperity and ~ occurred in the same eight 
segments, their mutual information score would be 
5.6. If they never occur in the same segments, their 
m would be negative infinity. Here, for prosperity/~ 
~, m = 5.077 which shows that these two words are 
indeed highly correlated. 
The t-score was used as a confidence measure. We 
keep pairs of words if their t > 1.65 where 
t ~ Pr(Yl, Y2) - Pr(V1) Pr(Y2) 
For prosperity/~.~\]~, t = 2.33 which shows that 
their correlation is reliable. 
6 Results 
The English half of the corpus has 5760 unique words 
containing 2779 nouns and proper nouns. Most 
of these words occurred only once. We carried 
out two sets of evaluations, first counting only the 
best matched pairs, then counting top three Chinese 
translations for an English word. The top N candi- 
date evaluation is useful because in a machine-aided 
translation system, we could propose a list of up to, 
say, ten candidate translations to help the transla- 
tor. We obtained the evaluations of three human 
judges (El-E3). Evaluator E1 is a native Cantonese 
speaker, E2 a Mandarin speaker, and E3 a speaker of 
both languages. The results are shown in Figure 6. 
The average accuracy for all evaluators for both 
sets is 73.1%. This is a considerable improvement 
from our previous algorithm (Fung & McKeown 
1994) which found only 32 pairs of single word trans- 
lation. Our program also runs much faster than 
other lexicon-based alignment methods. 
We found that many of the mistaken transla- 
tions resulted from insufficient data suggesting that 
we should use a larger size corpus in our future 
work. Tagging errors also caused some translation 
mistakes. English words with multiple senses also 
tend to be wrongly translated at least in part (e.g., 
means). There is no difference between capital let- 
ters and small letters in Chinese, and no difference 
between singular and plural forms of the same term. 
This also led to some error in the vector represen- 
tation. The evaluators' knowledge of the language 
and familiarity with the domain also influenced the 
results. 
Apart from single Word to single word transla- 
tion such as Governor/~ and prosperity/~i~fl¢~, 
we also found many single word translations which 
show potential towards being translated as com- 
pound domain-specific terms such as follows: 
• finding Chinese words: Chinese texts do not 
have word boundaries such as space in English, 
therefore our text was tokenized into words by a 
statistical Chinese tokenizer (Fung & Wu 1994). 
Tokenizer error caused some Chinese characters 
to be not grouped together as one word. Our 
program located some of these words. For ex- 
ample, Green was aligned to ,~j~,/~ and -~ which 
suggests that ,~j~ could be a single Chinese 
word. It indeed is the name for Green Paper - 
a government document. 
• compound noun translations: carbon could 
be translated as \]i~, and monoxide as ~. If 
carbon monoxide were translated separately, we 
would get ~ --~K4h . However, our algorithm 
found both carbon and monoxide to be most 
likely translated to the single Chinese word --~ 
4h~ which is the correct translation for carbon 
monoxide. 
The words Legislative and Council were both 
matched to ~-¢r~ and similarly we can de- 
duce that Legislative Council is a compound 
noun/collocation. The interesting fact here is, 
Council is also matched to ~J. So we can deduce 
that ~-'r_~j should be a single Chinese word cor- 
responding to Legislative Council. 
• slang: Some word pairs seem unlikely to be 
translations of each other, such as collusion and 
its first three candidates ~(it pull), ~t~(cat), F~ 
(tail). Actually pulling the cat's tail is Can- 
tonese slang for collusion. 
The word gweilo is not a conventional English 
word and cannot be found in any dictionary 
but it appeared eleven times in the text. It 
was matched to the Cantonese characters ~, ~, 
~, and ~ which separately mean vulgar/folk, 
name/litle, ghost and male. ~ means 
the colloquial term gweilo. Gweilo in Cantonese 
is actually an idiom referring to a male west- 
erner that originally had pejorative implica- 
tions. This word reflects a certain cultural con- 
text and cannot be simply replaced by a word 
to word translation. 
• collocations: Some word pairs such as projects 
and ~(houses) are not direct translations. 
However, they are found to be constituent 
words of collocations - the Housing Projects (by 
the Hong Kong Government).Both Cross and 
Harbour are translated to 'd~Yff.(sea bottom), and 
then to Pi~:i(tunnel), not a very literal transla- 
tion. Yet, the correct translation for ~J-~ll~ 
is indeed the Cross Harbor Tunnel and not the 
Sea Bottom Tunnel. 
The words Hong and Kong are both translated 
into ~i4~, indicating Hong Kong is a compound 
name. 
Basic and Law are both matched to ~:~2~, so 
we know the correct translation for ~2g~ is 
Basic Law which is a compound noun. 
• proper names In Hong Kong, there is a 
specific system for the transliteration of Chi- 
nese family names into English. Our algo- 
241 
lexicons 
primary(l) 
secondary(l) 
total(l) 
primary(3) 
secondary(3) 
total(3) 
total word pairs 
128 
533 
661 
128 
533 
661 
correct pairs accuracy 
E1 E2 E3 E1 E2 E3 
101 107 90 78.9% 83.6% 70.3% 
352 388 382 66.0% 72.8% 71.7% 
453 495 472 68.5% 74.9% 71.4% 
112 101 99 87.5% 78.9% 77.3% 
401 368 398 75.2% 69.0% 74.7% 
513 469 497 77.6% 71.0% 75.2% 
Figure 4: Bilingual lexicon compilation results 
rithm found a handful of these such as Fung/~g, 
Wong/~, Poon/~, Hui/ iam/CY¢, Tam/--~, etc. 
7 Conclusion 
Our algorithm bypasses the sentence alignment step 
to find a bilingual lexicon of nouns and proper nouns. 
Its output shows promise for compilation of domain- 
specific, technical and regional compounds terms. It 
has shown effectiveness in computing such a lexicon 
from texts with no sentence boundary information 
and with noise; fine-grain sentence alignment is not 
necessary for lexicon compilation as long as we have 
highly reliable anchor points. Compared to other 
word alignment algorithms, it does not need a pri- 
ori information. Since EM-based word alignment 
algorithms using random initialization can fall into 
local maxima, our output can also be used to pro- 
vide a better initializing basis for EM methods. It 
has also shown promise for finding noun phrases in 
English and Chinese, as well as finding new Chinese 
words which were not tokenized by a Chinese word 
tokenizer. We are currently working on identifying 
full noun phrases and compound words from noisy 
parallel corpora with statistical and linguistic infor- 
mation. 

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