Mining Parallel Texts from Mixed-Language Web Pages
Masao Utiyama
Daisuke Kawahara
Keiji Yasuda
Eiichiro Sumita
National Institute of Information and Communications Technology (NICT)
Keihanna Science City 619-0288 Kyoto, Japan{mutiyama, dk, keiji.yasuda, eiichiro.sumita}@nict.go.jp

Abstract
We propose to mine parallel texts from mixedlanguage web pages. We define a mixedlanguage web page as a web page consisting of (at least) two languages. We minedJapanese-English parallel texts from mixedlanguage web pages. We presented the statistics for extracted parallel texts and conductedmachine translation experiments. These statistics and experiments showed that mixedlanguage web pages are rich sources of parallel texts.

1

Introduction

Parallel corpora are indispensable language resources for multi-lingual natural language processing, such as corpus-based machine translation (MT)(Nagao, 1981; Brown et al., 1993) and cross-lingual
information retrieval.
However, there are relatively few widely available parallel corpora. These include the ArabicEnglish and Chinese-English parallel corpora distributed by the Linguistic Data Consortium (Ma andCieri, 2006); the Europarl corpus (Koehn, 2005),
which consists of 11 European languages; the JRCAcquis corpus, which consists of more than 20 European languages (Steinberger et al., 2006); and aJapanese-English patent parallel corpus (Utiyama
and Isahara, 2007). Although these parallel corpora
are large scale, they are limited in the language registers and language pairs that they cover.
Much work has been undertaken to overcome thislack of parallel corpora. For example, Resnik andSmith (2003) have proposed mining the web to collect parallel corpora for low-density language pairs.

Zhao and Vogel (2002), Utiyama and Isahara (2003),
Fung and Cheung (2004), and Munteanu and Marcu(2005) have extracted parallel sentences from comparable or non-parallel corpora.
In this paper, we mine parallel texts from the web(Ma and Liberman, 1999; Resnik and Smith, 2003;
Shi et al., 2006). The novel contribution of our work
compared to previous work is that we propose tomine parallel texts from mixed-language web pages.
We define a mixed-language web page as a web
page consisting of (at least) two languages. We
mine Japanese-English parallel texts from mixedlanguage web pages consisting of Japanese and English texts.
In contrast to our work, previous studies havemined parallel texts from parallel web pages. A pairof parallel web pages consists of two monolingual
web pages in different languages with almost thesame meaning. For example, Shi et al. (2006) havealigned parallel English and Chinese web pages andaligned sentences in these aligned pages.
We verify that mixed-language web pages are richsources of parallel texts. Our work complements theprevious work. By combining our work with the previous work, it will be possible to mine parallel texts
from both mixed-language web pages and parallel
web pages.
In Section 2, we describe how we mine paralleltexts from mixed-language web pages. In Section
3, we show basic statistics for the parallel texts obtained. In Section 4, we use the extracted paralleltexts to improve the performance of an SMT system.

2

Mining parallel texts

2.1 A pilot sutdy
The degree of parallelness of mixed-language web
pages is wide. First of all, most Japanese web pages
that contain both English and Japanese texts do notcontain parallel texts.
As a pilot study, we examined 10,000 Japanese
web pages to see how many of them contained atleast one English sentence. We found that about
20% of them contained English sentences. Twenty
percent is a fairly large percentage. However, wefound no web pages that contained parallel texts inthese 10,000 pages. The English sentences contained in these pages were conventional ones, suchas “All rights reserved,” which had no counterpartsin Japanese.
From this experiment, we estimated the percentage of mixed-language web pages containing paral1
lel texts to be 0.01% (∼ 10000+2
× 100) according
to Laplace’s Law (Manning and Schütze, 1999).
This pilot study shows that we need to impose
some constraints on the pages we search for parallel texts. In Section 2.2, we describe how we obtain
mixed-language web pages.
2.2 Obtaining mixed-language web pages
We need mixed-language web pages that are suitable for mining parallel texts. We adopt the following procedure to obtain such mixed-language web
pages, which consist of Japanese and English texts:(1) Crawl Japanese web pages, (2) process each web
page, and (3) extract mixed-language web pages
based on several constraints.
2.2.1 Crawl Japanese web pages
We first crawl Japanese web pages. Japanese web
pages are efficiently crawled by using a web crawler
with a Japanese filter. This Japanese filter uses themeta information of web pages (HTML), and a linguistic characteristic of the Japanese language inthe same way as the method used for constructing
a Japanese web corpus (Kawahara and Kurohashi,2006).
(1) Check that the “charset” of the HTML header
is one of the Japanese encodings: euc-jp, x-euc-jp,
iso-2022-jp, shift jis, windows-932, x-sjis, shift-jp,
shift-jis, or utf-8(2) If the charset is utf-8, the web page is possibly written in a non-Japanese language. To extract
only Japanese pages, check for the existence of aJapanese postposition (ga, wo, ni, ha, no or de) inthe HTML body.
We crawled 100 million Japanese pages in thisstudy.
2.2.2

Process each web page

We extract text portions from each web page, andsplit them into sentences. Then, we judge whether
each sentence is Japanese or English.
(1) Split a web page into sentences: A web pageis split into sentences using periods and HTML tags,
such as “br” and “p”.
(2) Judge whether a sentence is Japanese or English: On Japanese web pages, sentences that are notwritten in Japanese are mostly English sentences.
Therefore, we assume that non-Japanese sentences
are written in English when judging whether a sentence is Japanese or English. We consider a sentence to be English if it satisfies all of the following
four conditions, otherwise it is judged as Japanese:1(A) The sentence does not contain Japanese-specificcharacters such as HIRAGANA, KATAKANA andKANJI. (B) The sentence contains at least one whitespace. (C) The sentence ends with “.”, “?” or “!”.
(D) More than 90% of the characters in the sentence
match [a-zA-Z,.?! ].
The results of this process could contain noisy English or Japanese sentences. That is, non-English
or non-Japanese sentences could be judged as English or Japanese sentences. However, the alignment method described in Section 2.3 is able toextract clean English-Japanese sentence alignmentsfrom these potentially noisy sentences, as verified in
Section 3.
2.2.3

Extract mixed-language web pages

If we merely extract mixed-language web pages
that contain an English sentence, we erroneously obtain web pages that just contain conventional English sentences, such as “All rights reserved,” whichhave no counterparts in Japanese. Therefore, we impose the following two constraints to extract mixed1

We impose strong conditions for English judgment, because we use the number of English sentences as a constraint
in the subsequent method.

language web pages that possibly contain parallel
sentences: (1) The web page contains one of the following 10 Japanese words (in KANJI) that imply theexistence of translations, eigo (English), hon’yaku(translation), wayaku (Japanese translation), eiyaku(English translation), eikaiwa (English conversation), eibun (English sentence), taiyaku (translationpair), yakubun (translation), nihongoyaku (Japanesetranslation), and houyaku (Japanese translation). (2)The web page contains more than N English sentences. We conducted a preliminary experiment andempirically determined N to 10.2
Finally, we applied the above method to the 100
million Japanese web pages. As a result, 113,420
mixed-language web pages were obtained.

tence alignments (J1 , E1 ), (J2 , E2 ), . . . (Jm , Em )using DP matching, where Ji and Ei were Japaneseand English sentences in J and E. We allowed 1-ton, n-to-1 (0 ≤ n ≤ 5), or 2-to-2 alignments whenaligning the sentences. The similarity between Ji
and Ei (SIM(Ji , Ei )) was calculated based on word
overlap (i.e., number of word pairs from Ji and Ei
that were translations of each other based on a bilingual dictionary with 450,000+ entries).
We next calculated the similarity between J andE (AVSIM(J, E)) as defined by (Utiyama and Isahara, 2003), using:

2.3 Alignment procedure

A high AVSIM(J, E) value occurs when the sentence alignments in J and E have high similarity
values.
We also calculated the ratio of the numbers of sentences between J and E (R(J, E)) using:

We selected Utiyama and Isahara’s alignment
method (Utiyama and Isahara, 2007) from various
methods for aligning comparable or noisy paralleltexts (Zhao and Vogel, 2002; Fung and Cheung,
2004; Munteanu and Marcu, 2005). This is because
their method has been successfully applied in aligning Japanese-English noisy parallel texts (Utiyama
and Isahara, 2003; Utiyama and Isahara, 2007) and
we could use their tool off-the-shelf.
In order to apply their method to mixed-language
web pages, we converted these pages into noisy parallel text files. That is, given a web page containing Japanese and English texts, we made a Japanese
text file and an English text file from the web
page.3 We regarded these two text files as a pairof noisy parallel text files and applied Utiyama andIsahara’s method to these. In the following, we
briefly describe how we applied Utiyama and Isahara’s method to these parallel texts. See (Utiyama
and Isahara, 2007) for details of their method.
We first aligned the sentences in each pair of noisy
parallel text files by using a standard dynamic programming (DP) matching method (Gale and Church,
1993; Utsuro et al., 1994). That is, let J and E bea Japanese text file and an English text file, respectively, we calculated the maximum similarity sen2
Our mining method will not be much affected by N because our method can extract parallel sentences very accurately
as shown in Section 3.
3
We simply extracted Japanese (English) sentences from theweb page and put them into a Japanese (English) text file.

Pm

AVSIM(J, E) =i=1 SIM(Ji , Ei )R(J, E) = min(m

|J| |E|,)|E| |J|(1)(2)

where |J| is the number of sentences in J, and |E| isthe number of sentences in E. A high R(J, E) value
occurs when |J| ∼ |E|. Consequently, R(J, E) canbe used to measure the literalness of translation between J and E in terms of the ratio of the number ofsentences.
Using AVSIM(J, E) and R(J, E), we defined thesimilarity between J and E (AR(J, E)) as

AR(J, E) = AVSIM(J, E) × R(J, E)(3)

Finally, we defined the score of alignment Ji and
Ei as
Score(Ji , Ei ) = SIM(Ji , Ei ) × AR(J, E)(4)

A high Score(Ji , Ei ) value occurs when (1) sentences Ji and Ei are similar, (2) documents J andE are similar, and (3) numbers of sentences |J| and|E| are similar. Score(Ji , Ei ) combines both sentence and document similarities to discriminate between correct and incorrect alignments.

3

Statistics for extracted parallel texts

In this section, we evaluate the performance of thealignment method described in the previous section.
We applied the alignment method to the 113,420
mixed-language web pages obtained from the 100
million Japanese web pages.
3.1 Basic statistics
We first show summary statistics for the 113,420
mixed-language web pages in Table 1. The “Sentences” row shows the total number of sentences in
the 113,420 pages and the “S. mean” row shows
the average number of sentences on a page. The“Words” and “W. mean” rows show similar statistics for words. The figures in the “English” and“Japanese” columns are the figures for “English”
and “Japanese” texts, respectively.4
SentencesS. meanWords
W. meanEnglish21,302,046
188
87,249,745
769Japanese
66,355,812
585
1,063,265,797
9,375

Table 1: Summary statistics

Table 1 shows that the amount of Japanese texts
was much larger than that of English. This is because the 100 million web pages were crawled frommainly monolingual Japanese web pages. This table suggests that the 113,420 mixed-language web
pages were very noisy parallel texts.
3.2 Parallelness of mined web pages
In order to see the parallelness of the mined web
pages, we used the AR values (Equation 3) that wereassigned to the 113,420 web pages.
We sorted the web pages in decreasing order of
their AR values. We divided these pages into seven
ranges according to their ranks, as shown in the“Range” column of Table 2. Each of the first six
ranges contained 3000 pages and the last one contained the remaining 95,420 pages.
We extracted 50 web pages from each range5 and4

We used a Japanese morphological analyzer, ChaSen, tosegment Japanese texts into words.
5
When we randomly sampled web pages, we discarded sample pages if they contained more than 1000 sentences, in order

evaluated each of web pages6 as:• Ap if that page contained parallel texts andthose parallel texts were at least 50% of thetexts on that page,• Bp if that page contained parallel texts andthose parallel texts were less than 50% of thetexts on that page, or• Xp if that page did not contain parallel texts.
The evaluation results are shown in Table 2. The“Ap ”, “Bp ”, and “Xp ” columns show the numbersof samples that were evaluated as the correspondinglabels. The figures in the “Ap +Bp (%)” are the percentages of the samples evaluated as Ap or Bp .
This table shows that samples taken from therange “18001 –” did not contain Ap web pages. Thismeans that almost all of the Ap web pages wereranked in the first 18,000 pages (the first 15.9%
18000
× 100) of all pages). This suggests that AR(= 113420
values are very effective for ranking Ap web pages.
Next, the ratio of the Ap or Bp web pages in thefirst 18,000 pages was about 73%. From this figure,
we estimated the number of Ap or Bp web pages in
the first 18,000 pages as 13,200 (= 18000×0.733..).
We also estimated the number of Bp web pages in
the remaining 95,420 pages as 20,992 (= 95420 ×
11
50 ).
Note that the percentage of mixed-language web
pages containing parallel texts was estimated to be
0.01% in Section 2.1. In this section, we have shownthat about 13,200 pages are Ap or Bp pages in thefirst 18,000 pages. These 13,200 pages are 0.0132%
of the 100 million web pages. This means that wehave extracted a significant subset of parallel mixedlanguage pages in the first 18,000 pages.
These figures and Table 2 show that we can rank
Ap or Bp web pages highly using AR values (theprecision is good) but we also miss some Bp web
pages if we use only highly ranked web pages (therecall could be improved). Overall, we concluded
that we can use AR values for ranking Ap or Bp web
pages highly.
to reduce the evaluation workload. As a result, we discarded 22
pages in the process of sampling 350 pages.
6
The evaluation conducted in Sections 3.2 and 3.3 were performed by an expert evaluator who has been performing similarjobs more than five years.

Range1 – 3000
3001 – 6000
6001 – 9000
9001 – 12000
12001 – 15000
15001 – 18000
18001 –

Ap
39
36
30
28
10
12
0

Bp
4
10
13
11
15
12
11

Xp
7
4
7
11
25
26
39

Ap +Bp (%)
86
92
86
78
50
48
22

Table 2: Parallelness of mined web pages

Next, we examined genres of the 231 Ap or Bp
web pages in Table 2, in order to examine what kindsof parallel texts were on a single page. We found 13
genres. The top 5 genres were (1) personal opinion (blogs, chat, email etc), (2) computer (software
manuals, online games, etc), (3) example sentences,(4) book, and (5) daily conversation. These results
show that the genres of mixed language web pages
are wide.
3.3 Accuracy of sentence alignments
We show the accuracy of sentence alignments inthis section. Our sentence alignments were obtained as follows. First, we obtained 6.3 million
one-to-one sentence alignments, as a result of applying the alignment method described in Section
2.3. This was about 30% of the English sentences
according to Table 1. Next, we removed the alignments whose English sentences did not end with periods, exclamation marks, or question marks to reduce alignment pairs considered as noise.7 We also
removed some sentence pairs that were too imballength of longer sentence
anced ( length of shorter sentence > 3). Finally, weremoved all but one of the identical alignments. Two
individual alignments were determined to be identical if they contained the same Japanese and English
sentences. Consequently, 929,011 alignments wereobtained.
We sorted these sentence alignments in decreasing order of Score in Equation 4. We divided thesealignments into five ranges according to their ranks,
as shown in the “Range” column of Table 3. Eachof the first four ranges contained 100,000 sentence
7

The tool we used for alignment automatically re-segmented
Japanese and English texts. As a result, the sentence segmentations obtained in Section 2.2.2 were changed. Consequently,
we again needed to remove noisy English sentences.

alignments and the last one contained the remaining
529,011 alignments.
We extracted 100 sentence alignments from eachrange and evaluated each alignment as:• As if 80% or more of the contents were shared
between the English and Japanese sentences,• Bs if 50% or more and less than 80% of thecontents were shared• Cs if less than 50% of the contents were shared(The English and Japanese sentences shouldshare some contents, but the amount of theshared contents were less than 50%),• Xs if the meanings of the Japanese and English
sentences were totally different.
The evaluation results are shown in Table 3. The
figures in the “As ”, “Bs ”, “Cs ”, and “Xs ” columns
are the number of samples that were evaluated as thecorresponding labels.
Range
1- 100,000
100,001 – 200,000
200,001 – 300,000
300,001 – 400,000
400,001 –

As
88
71
37
7
1

Bs
9
9
4
3
0

Cs
2
4
7
1
0

Xs
1
16
52
89
99

Table 3: Accuracy of sentence alignments

This table shows that most of the As alignments
were extracted in the first 300,000 alignments. Thissuggests that Score in Equation 4 ranked good sentence alignments highly.
The percentages of the As alignments in theranges “1 – 100,000” and “100,001–200,000” were
88% and 71%, respectively. This suggests that thefirst 200,000 sentence alignments were effective formulti-lingual natural language processing.
Based on the statistics presented in Sections 3.2
and 3.3, we concluded that we extracted a clean parallel corpus from the original web corpus.

4 Machine translation experiments
We verify the usefulness of the extracted sentencealignments for SMT in this section.
We used a state-of-the-art phrase-based SMT system (Finch and Sumita, 2008), which is comparable

in performance to the MOSES system (Koehn et al.,2007). To train SMT models, we used a training
toolkit adapted from the MOSES system. We used
GIZA++ (Och and Ney, 2003) for word alignment
and SRILM (Stolcke, 2002) for language modeling.
We used 5-gram language models trained with modified Kneser–Ney smoothing. Minimum error rate
training was used to tune the decoder’s parameterson the basis of the bilingual evaluation understudy(BLEU) score (Papineni et al., 2002), and tuning
was performed using the standard technique developed by Och (Och, 2003).
We used the development data for the IWSLT2007 Japanese-English translation task (Fordyce,2007) to verify the usefulness of the extracted sentence alignments. The development data consistedof five sets, devset1, devset2, devset3, devset4, anddevset5. Each of these data sets had about 500 sentences. The numbers of reference translations were
16 for devset1, devset2, and devset3 and 7 for devset4 and devset5.
We used devset1 to tune the SMT system and used
devset2, devset3, devset4, and devset5 as the testsets
to evaluate the performance of the SMT system interms of BLEU scores. Hereafter, we refer to devset2, ..., devset5 as set2, ..., set5, respectively.
In the following experiments, we only change thetraining data that were used for making our language
and translation models, in order to compare various
parallel texts.

dicates the mean value of the BLEU scores for these4 testsets.

4.1 Relationship between alignment data size
and BLEU scores

Next, we used the training data for the IWSLT-2007
workshop. It consisted of about 40,000 EnglishJapanese parallel sentences. We used this training
data to make our language and translation models.
The BLEU scores are shown in Table 4. The values
in the “Mean” column are the averages of the BLEU
scores for set2, set3, set4 and set5.

As described in Section 3.3, we have extracted about
900,000 sentence alignments. In this section, we increase the size of training data to see how MT performance evolves using more data.
First, we made an English 5-gram language modelfrom the first 900,000 sentence alignments. Next,
we used the first 100,000, 200,000, ..., 900,000 sentence alignments to make our translation models (aphrase-table and reordering table). Each of thesemodels was used with the same 5-gram language
model.
The BLEU scores for these settings are shown in
Figure 1. The lines entitled “set2,” “set3,” “set4,”and “set5” indicate the BLEU scores for these testsets. The middle line, which is entitled “mean,” in-

40
set2
set3
set4
set5
mean

35

BLEU

30

25

20

15

10
100

200

300

400
500
600
Alignment data size (K)

700

800

900

Figure 1: Relationship between alignment data size and
BLEU scores

Figure 1 shows that the highest mean BLEU score
was obtained when using the first 200,000 sentencealignments. The BLEU scores decreased when weused more than 200,000 alignments.
It is interesting that this observation is consistent
with that in Section 3.3. In that section, we observed that the first 200,000 sentences were relatively clean parallel sentences. In this section, we
showed that using 200,000 sentences resulted in thehighest BLEU score. This suggests that adding
noisy parallel sentences is detrimental to improving
BLEU scores.
4.2

IWSLT training data

set2
58.70

set3
58.15

set4
24.55

set5
20.48

Mean
40.47

Table 4: Results for the IWSLT training data

Figure 1 and Table 4 show that the performanceof the SMT systems trained with our extracted sentence alignments are inferior to that of the SMT system trained with the IWSLT training data. A likely
reason is that the extracted alignments are out-of-

domain data with respect to the IWSLT testsets. In
the following, we show that the extracted alignments
are useful for improving the performance of the
SMT system trained with the IWSLT training data,
even though these alignments are not best suited tothe testsets.
4.3 Interpolation of models
We linearly interpolated8 language and translation
models (Foster and Kuhn, 2007) to improve the performance of the SMT system.
4.3.1 Interpolation of language models
We first interpolated language models (LMs). We
interpolated the language model made from 900,000
sentences in Section 4.1 (hereafter LM(900k)) andthat made from the IWSLT training data in Section 4.2 (hereafter LM(IWSLT)). The weight of
LM(IWSLT) was 0.1, 0.2, ..., 0.9. In addition tothese interpolated language models, we used thetranslation model made from the IWSLT training
data in Section 4.2 for all of the weights.
The figures in the 0.1, ..., 0.9 rows in Table 5 show
the BLEU scores for set2, ..., set5, along with themean values. The figures in the “IWSLT” row weretaken from Table 4. This table shows that the interpolation of the language models improved BLEU
scores for a wide range of interpolation weights.
weight
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
IWSLT

set2
58.35
57.67
59.25
58.70
58.87
58.12
58.73
56.94
58.73
58.70

set3
58.45
58.89
58.90
58.24
59.43
58.63
58.71
57.04
58.69
58.15

set4
26.10
26.52
25.99
25.05
26.12
25.14
24.85
22.93
24.75
24.55

set5
21.46
21.16
21.63
21.77
21.95
20.95
21.11
18.71
20.75
20.48

mean
41.09
41.06
41.44
40.94
41.59
40.71
40.85
38.91
40.73
40.47

Table 5: Results for interpolation of LMs

4.3.2 Interpolation of translation models
We next interpolated translation models (TMs).
We interpolated the translation model (a phrase8

Let p1 and p2 be two probabilities and w be the weight of
p1 , the linear interpolation of these probabilities is wp1 + (1 −w)p2 , where 0 ≤ w ≤ 1.

table and reordering-table) made from the first
200,000 sentences in Section 4.1 (hereafter
TM(200k)) and that made from the IWSLT training
data in Section 4.2 (hereafter TM(IWSLT)). The
weight of TM(IWSLT) was 0.1, 0.2, ..., 0.9. We
used LM(IWSLT) for all of the weights.
The figures in Table 6 shows the BLEU scores forthis setting. These shows that the interpolation of
the translation models improved BLEU scores for awide range of interpolation weights.
weight
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
IWSLT

set2
59.00
61.62
62.24
61.51
62.77
60.39
61.05
59.75
59.05
58.70

set3
55.62
58.61
59.89
60.53
60.53
58.24
58.92
58.20
59.22
58.15

set4
22.72
24.79
25.10
26.24
25.72
24.47
24.75
23.09
24.41
24.55

set5
19.18
20.86
21.09
21.89
21.42
20.08
21.22
19.09
20.86
20.48

mean
39.13
41.47
42.08
42.54
42.61
40.80
41.49
40.03
40.89
40.47

Table 6: Results for interpolation of TMs

4.3.3

Interpolation of both models

Finally, we interpolated both language and translation models. We interpolated LM(900k) andLM(IWSLT) with equal weights and interpolated
TM(200k) and TM(IWSLT) with equal weights.
The “LM&TM” row shows the BLEU scores for thissetting. The “IWSLT”, “LM”, and “TM” rows weretaken from Tables 4, 5, and 6, respectively. This table shows that “LM&TM” were not always better
than “LM” or “TM”. However, “LM&TM” were always better than “IWSLT.”
IWSLT
LM
TM
LM&TM

set2
58.70
58.87
62.77
61.93

set3
58.15
59.43
60.53
61.44

set4
24.55
26.12
25.72
25.79

set5
20.48
21.95
21.42
21.50

mean
40.47
41.59
42.61
42.67

Table 7: Results for interpolation of both models

Based on the experiments in Sections 4.3.1, 4.3.2,
and 4.3.3, we concluded that our mined sentencealignments are useful for improving the performanceof a state-of-the-art SMT system.

5Conclusion

The web is a rich source of parallel texts. Indeed,
researchers have already been mining parallel texts
from parallel web pages. In contrast, we have proposed to mine parallel texts from mixed-language
web pages. We have extracted parallel texts frommixed-language web pages containing Japanese andEnglish. We have presented the statistics for these
extracted parallel texts and conducted MT experiments. These statistics and experiments have shownthat mixed-language web pages are rich sources ofparallel texts.
Our work complements the previous work. By
combining our work and the previous work, it will
be possible to mine parallel texts from both mixedlanguage web pages and parallel web pages.

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