word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs

11/27/2019 ∙ by Yo Joong Choe, et al. ∙ Kakao Corp. 0

We present word2word, a publicly available dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018 (Lison et al., 2018). To obtain this dataset, we use a count-based bilingual lexicon extraction model based on the observation that not only source and target words but also source words themselves can be highly correlated. We illustrate that the resulting bilingual lexicons have high coverage and attain competitive translation quality for several language pairs. We wrap our dataset and model in an easy-to-use Python library, which supports downloading and retrieving top-k word translations in any of the supported language pairs as well as computing top-k word translations for custom parallel corpora.



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1 Introduction

Bilingual lexicons [fung1998statistical] are valuable resources for cross-lingual tasks, including low-resource machine translation [ramesh2018neural, gu2019pointer] and cross-lingual word embeddings [ruder2017survey]. However, it is often difficult to find a large enough set of bilingual lexicons that is freely and readily available across various language pairs [levy2017strong]. For example, standard bilingual dictionaries like Wiktionary111https://en.wiktionary.org often do not explicitly provide word correspondences but refers or redirects to the query word’s dictionary form:

  • Query: travaillé (French for ‘worked’)
    Result: (verb) past principle of travailler ‘work’

  • Query: 먹었다 (Korean for ‘ate’)
    Result: redirects to 먹다 ‘eat’

Not only does this make it tedious to find word-level correspondences across many query words, this is particularly problematic when we try to find word correspondences for languages where some dictionary forms are rarely used in ordinary discourse, such as the case of 먹다 in the Korean language.

While the task of bilingual lexicon extraction (BLE) has been popular in both early and recent literature, spanning from count-based approaches [fung1998statistical, vulic2013cross, liu2013topic] to using cross-lingual word embeddings [ruder2017survey, mikolov2013exploiting, gouws2015bilbowa, conneau2017word, levy2017strong, artetxe2018robust, artetxe2019bilingual], few were focused on building high-coverage bilingual lexicons across many language pairs, possibly including non-Indo-European languages. In fact, many of the recent studies and their accompanying packages [conneau2017word, artetxe2018robust, glavas2019properly] aim at evaluating cross-lingual word embeddings, so that they involve at most 10-100s of language pairs and 1-5K words for each pair.

Motivated by the lack of publicly available and high-coverage bilingual lexicons across diverse languages, we present word2word, a large collection of bilingual lexicons for 3,564 language pairs across 62 languages that is wrapped around an open-source and easy-to-use Python interface. We extract top- bilingual word correspondences from all parallel corpora provided by OpenSubtitles2018222http://opus.nlpl.eu/OpenSubtitles-v2018.php [lison2018opensubtitles2018], using a count-based model that takes into account both monolingual and cross-lingual co-occurrences. The package also provides interface for obtaining bilingual lexicons for custom parallel corpora in any other language pairs and domains not covered by OpenSubtitles2018.

2 The word2word Dataset


# Languages 62
# Language Pairs 3,564
Avg. Lexicon Size 127,023
Avg. # Translations Per Word 8.8


Table 1: Overview of the word2word dataset.


Language Pair Lexicon Size # Unique Translations Avg. # Translations Per Word # Sentences Used
Arabic-English 335.5K 86.0K 9.7 29.8M
English-Arabic 97.6K 191.6K 9.5 29.8M
S.Chinese-English 214.0K 87.0K 9.5 11.2M
English-S.Chinese 101.6K 139.1K 9.4 11.2M
T.Chinese-English 201.7K 72.5K 9.5 4.8M
English-T.Chinese 85.8K 119.7K 9.2 4.8M
French-English 92.1K 59.1K 9.8 41.8M
English-French 72.1K 71.4K 9.7 41.8M
Italian-English 111.5K 63.9K 9.7 35.2M
German-English 127.0K 64.8K 9.7 22.5M
English-German 73.6K 95.9K 9.6 22.5M
English-Italian 75.4K 83.9K 9.6 35.2M
Japanese-English 83.3K 75.2K 9.2 2.1M
English-Japanese 102.1K 63.8K 9.3 2.1M
Korean-English 87.2K 75.8K 9.3 1.4M
English-Korean 105.5K 69.8K 9.1 1.4M
Russian-English 213.4K 68.7K 9.7 25.9M
English-Russian 76.2K 155.8K 9.5 25.9M
Spanish-English 107.1K 60.8K 9.8 61.4M
English-Spanish 73.9K 82.5K 9.7 61.4M
Thai-English 155.6K 84.2K 9.4 3.3M
English-Thai 109.2K 99.2K 9.2 3.3M
Vietnamese-English 96.6K 76.6K 9.0 3.5M
English-Vietnamese 96.4K 75.3K 9.3 3.5M


Table 2: Summary statistics for the word2word dataset between selected languages and English. Lexicon size refers to the number of unique words in source language for which translations exist. S.Chinese and T.Chinese refer to simplified and traditional Chinese, respectively.


Word Top-5 Translations
English French
exceptional exceptionnel exceptionnelle exceptionnels exceptionnelles exception
whether plaise décider importe question savoir
committee comité éthique accueil commission central
clown clown clowns bouffon guignol cirque
spread dispersez-vous propagation répandre propager répandu
French English
hobbs hobbs abigail garret jacob garrett
mêlé mixed involved middle part murder
établir establish establishing set able connection
taule slammer joint locked jail prison
chaussettes socks sock stockings pairs underwear
English Korean
slaughtered 학살 도륙 도살 당했 살육
shadow 그림자 그늘 알맞 어둠 존재
Charles 찰스 제프리 Charles 조프리 램퍼트
concerns 걱정 우려 염려 관한 판단력
reverse 뒤집 후진 거꾸로 되돌리
Korean English
아유 arm Thank thrilled killing NamWon
상어 shark Shark sharks Tank Tiger
rat rats mouse mice squeeze
기꺼이 willing happy pleasure gladly willingly
어떤 Some kind which any anything


Table 3: Randomly sampled words and their top-5 translations in the EnglishFrench and EnglishKorean word2word bilingual lexicons. Top-5 translations are listed in the descending order of scores.

2.1 Data Statistics

The word2word dataset spans across 3,564 directed language pairs between 62 languages in the OpenSubtitles2018 dataset, a collection of translated movie subtitles extracted from OpenSubtitles.org333http://www.opensubtitles.org/. By design, our methodology covers 100% of words present in the source sentences, making the lexicon size much larger than existing bilingual dictionaries. The lexicon also contains up to top-10 word translations in the target language. We provide an overview of the entire dataset in Table 1.

In Table 2, we provide summary statistics for bilingual lexicons between English and some of the major languages (both European and non-European). For each pair, the lexicon size ranges from 76.2K (English-Russian) to 335.5K (Arabic-English), demonstrating the broad coverage of words in the dataset. For each of these words, the dataset includes an average of 9 or more highest-scored translations according to our extraction approach described in Section 3.1 Lexicon size for all language pairs can be found in Appendix B.

2.2 Examples

In Table 3, we present samples of top-5 word translations in the EnglishFrench and EnglishKorean bilingual lexicons. For each language pair, we randomly sample five words from the top-10,000 frequent words in the source lexicon and provide their top-5 word translations. This is to show translations for words that are relatively more likely used than others in typical discourse.

3 Methodology

3.1 Bilingual Lexicon Extraction

Bilingual lexicon extraction (BLE) is a classical natural language task where the goal is to find word-level correspondences from a (parallel) corpus. There are many different approaches to BLE, such as word alignment methods [brown1993mathematics, vogel1996hmm, koehn2007moses] and cross-lingual word representations [ruder2017survey, mikolov2013exploiting, liu2013topic, gouws2015bilbowa, conneau2017word].

Among them, we focus on simple approaches that can work well with various sizes of parallel corpora that are present in OpenSubtitles2018, which ranges from 129 sentence pairs in Armenian-Indonesian to 61M sentence pairs in English-Spanish. In particular, we avoid methods that require high-resource parallel corpora (e.g., neural machine translation) or external corpora (e.g., unsupervised or semi-supervised cross-lingual word embeddings). Also, since bilingual word-to-word mappings are hardly one-to-one

[fung1998statistical, somers2001bilingual, levy2017strong], we consider methods that yield relevance scores between every source-target word pair, such that we can extract not just one but the top- correspondences. For these reasons, we consider approaches based on (monolingual and cross-lingual) co-occurrence counts: co-occurrences, pointwise mutual information (PMI), and co-occurrences with controlled predictive effects (CPE).

3.1.1 Co-occurrences

The simplest baseline for our goal is to compute the co-occurrences between each source word and target word . For each source word , we can score any target word

based on the conditional probability



where denotes the number of (co-)occurrence counts of the word or word pair across the parallel corpus. The top- translations of source word can be computed as the top- target words with respect to their co-occurrence counts with .

3.1.2 Pointwise Mutual Information

Another simple baseline is pointwise mutual information (PMI), which further accounts for the monolingual frequency of a candidate target word :


Compared to the co-occurrence model in (1), PMI can help prevent stop words from obtaining high scores.

The use of PMI has been connected to the skip-gram with negative sampling (SGNS) [levy2014neural] model of word2vec [mikolov2013distributed]. PMI can also be interpreted as a conditional version of TF-IDF [fung1998statistical].

3.1.3 Controlled Predictive Effects

Figure 1: A schematic graphical model of English and French words. Co-occurrence and PMI models focus on the relationship from apple to pomme (A). CPE further controls for the confounding effect of other collocates like the (B) and juice (C).


Metric (%) Method en-es es-en en-fr fr-en en-de de-en en-ru ru-en en-zh zh-en en-it it-en
# Sentence Pairs 61.4M 41.8M 22.5M 25.9M 4.8M 35.2M
P@1 Co-occurrence 22.3 25.5 18.7 21.9 10.5 23.5 3.3 11.4 5.4 3.8 24.9 24.1
PMI 72.7 72.3 73.9 72.1 62.1 71.9 32.8 55.0 24.8 33.1 68.1 69.5
MUSE 81.7 83.3 82.3 82.4 74.0 72.4 51.7 63.7 42.7 37.5 66.2 58.7
CPE 82.4 79.5 83.6 80.7 82.4 81.1 66.7 68.9 56.0 58.7 80.9 82.1
P@5 Co-occurrence 67.8 71.4 63.1 66.3 63.7 65.5 52.3 51.8 46.0 36.3 61.9 68.5
PMI 92.3 90.4 92.5 90.1 90.5 88.1 74.1 79.5 58.7 66.1 90.3 91.1
MUSE - - - - - - - - - - 80.4 76.5
CPE 90.1 88.4 91.7 89.3 90.7 87.7 79.5 80.0 73.5 72.8 89.8 89.9


Table 4: Precision (%) on 1,500 word translations (test split from MUSE) for language pairs evaluated in the MUSE paper. P@1 and P@5 denote the precision of top-1 and top-5 predictions, respectively. The ISO 639-1 language codes are used (en: English, es: Spanish, fr: French, de: German, ru: Russian, zh: traditional Chinese, it: Italian).

While conditional probability and PMI are proportional to cross-lingual co-occurrence counts, they can fail to distinguish exactly which source word in the sentence is the most predictive of the corresponding target word in the translated sentence. For example, given a English-French pair (the apple juice, la jus de pomme), these baseline methods cannot isolate the effect of apple, as opposed to the and juice, on pomme.

To deal with this issue, we add a correction term that averages the probability of seeing given a confounder in the source language, i.e. . This probability is then weighted by the probability of actually seeing that confounder, i.e. . This correction can be explained intuitively by the dashed arrows in the schematic graphical model in Figure 1– it reflects the conditional independence relationships between words that the baseline models do not. We call the resulting approach as the method of controlled predictive effects (CPE).

Formally, we define the corrected CPE score as follows:


where is the source vocabulary and denotes the CPE term of any other source word when predicting from . Formally, this term is defined as


This CPE term measures the effect of additionally seeing (apple) when predicting (pomme), after controlling for the effect of any other (the), which the model views as a confounder. If , then , meaning that after observing a confounder , is no longer related to . The CPE term for each confounder is then marginalized over all possible confounders to give a final score, weighted by the probability of seeing that confounder in a sentence with . Note that , meaning that, after seeing when predicting , there is no additional effect by seeing (again).

In practice, summing the CPE scores over all words in the source vocabulary can be inefficient. Because many of the (unrelated) words in the vocabulary do not play a role in the confounding, we select the top- source words with the highest co-occurrence counts and correct for their effects only. We used in our experiments and found that using a larger did not make a meaningful difference on the quality of top-1 and top-5 correspondences.

3.1.4 Evaluation on MUSE Bilingual Dictionaries

We first evaluate the methods on the same ground-truth bilingual dictionaries as MUSE444https://github.com/facebookresearch/MUSE, a cross-lingual neural embedding model. Each dictionary contains 1,500 words and their translations obtained using an internal translation tool from the authors. Although we consider MUSE’s performance as a reference, we do note that it is difficult to make a fair comparison against MUSE: the count-based methods use parallel corpora from OpenSubtitles2018, while MUSE embeddings are instead learned from monolingual Wikipedia data (for its unsupervised version) and an additional 5,000-word bilingual lexicon (for its supervised version).

In Table 4, we report the top-1 and top-5 precision scores (P@1 and P@5, respectively) of the count-based methods and MUSE embeddings across twelve555The MUSE paper also presents the results on English-Esperanto and Esperanto-English, but the ground-truth dictionary is no longer available online. See https://github.com/facebookresearch/MUSE/issues/34. directed language pairs that were used to evaluate MUSE in its paper [conneau2017word]: English-Spanish, English-French, German-English, English-Russian, English-Chinese (traditional), and English-Italian, all in both directions. For MUSE, we report its best reported performance (only top-1 precision is reported, except for en-it and it-en) among its supervised and unsupervised variants.

Our main finding is that the CPE method consistently and significantly outperforms the co-occurrence and PMI baselines at top-1 precision score. We also find that CPE outperforms MUSE on most of the reported language pairs, especially when the number of sentence pairs is comparatively small (e.g., 13-21% improvement between English and Chinese, for which there are about 6% as many sentence pairs as those between English and Spanish). In terms of the top-5 precision score, the CPE method performs comparatively well with the PMI method, which performs better on some of the selected language pairs. Compared to the CPE method, we suspect that the PMI method overly favors rare words because it directly penalizes word counts, so that the most likely correspondence (which isn’t necessarily the least common) is pushed back to later ranks. More examples can be found in Appendix A


Metric (%) Method en-ar ar-en en-zh zh-en en-ja ja-en en-ko ko-en en-th th-en en-vi vi-en
# Sentence Pairs 29.8M 11.2M 2.1M 1.4M 3.3M 3.5M
P@1 Co-occurrence 23.3 1.1 2.1 0.4 5.0 0.3 22.9 0.4 0.6 0.5 4.0 2.1
PMI 13.3 20.7 8.5 20.6 33.5 16.7 14.0 14.9 18.3 13.4 20.5 16.5
CPE 30.3 27.9 48.3 34.3 49.3 40.4 39.1 38.1 48.1 31.0 30.0 37.7
P@5 Co-occurrence 46.9 35.2 50.5 27.1 30.7 29.1 36.6 26.9 55.6 24.4 39.3 28.3
PMI 57.0 61.6 78.7 65.3 64.0 60.5 48.8 57.7 64.5 52.7 50.1 60.4
CPE 58.1 50.5 80.9 60.1 66.8 66.4 54.9 60.0 69.3 53.1 48.9 62.2


Table 5: Precision (%) on 2,000 word translations between six non-European languages and English (source words randomly sampled from OpenSubtitles2018; gold labels taken from Google Translate). P@1 and P@5 denote the precision of top-1 and top-5 predictions, respectively. The ISO 639-1 language codes are used (ar: Arabic, zh: simplified Chinese, ja: Japanese, ko: Korean, th: Thai, vi: Vietnamese).


Language Python Tokenizer Module Reference
Arabic pyarabic.araby [zerrouki2012pyarabic]
Chinese (Simplified) Mykytea [neubig2011pointwise]
Chinese (Traditional) jieba n/a
Japanese Mykytea [neubig2011pointwise]
Korean konlpy.tag.Mecab [park2014konlpy]
Thai pythainlp n/a
Vietnamese pyvi n/a
Others nltk.tokenize.TokTokTokenizer [bird2009natural, dehdari2014neurophysiologically]


Table 6: List of Python tokenizer modules used for each language.

3.1.5 Evaluation on Non-European Languages

Next, we compare the performance of co-occurrence, PMI, and CPE methods on language pairs between English and some of the major non-European languages: Arabic, simplified Chinese, Japanese, Korean, Thai, and Vietnamese. As we detail in Section 3.2, these languages commonly require special word segmentation techniques. Also, they typically have relatively smaller amounts of sentences paired with English, making it more challenging for the models to achieve high precision.

Unfortunately, we learned in our early experiments that the MUSE test set translations are far from being perfect for these non-European languages. For example, in English-Vietnamese, we found that 80% of the 1,500 word pairs in the test set had the same word twice as a pair (e.g. crimson-crimson, Suzuki-Suzuki, Randall-Randall). Thus, for the non-European languages, we instead evaluate on translations using Google Translate666https://translate.google.com/, a proprietary777We note that, because Google Translate is proprietary and not open-source, its results may change depending on the time of access. Our evaluations use Google Translate results accessed on July 19, 2019. web software for machine translation. To construct this test set, we first sample 2,000 words from the monolingual word distribution of that language pair’s OpenSubtitles2018 parallel corpus. We use temperature-based smoothing () for the distribution to include more low-frequency words in the test set and also filter out words that include characters not from its alphabet (e.g., Charles in Korean). Then, for each of the 2,000 sampled words, we retrieve “common” and “uncommon” translations888For word translations, Google Translate categorizes its translations to three categories: common, uncommon, and rare translations. from Google Translate and treat them as ground truth labels.

The results are summarized in Table 5. Here, we see more evidence that the CPE method performs significantly better than both the co-occurrence and the PMI methods in top-1 precision as well as top-5 precision. The performance gap tends to be larger both when the language’s words are not whitespace-separated (e.g., Chinese and Japanese) and when there are a relatively small number of paired sentences (e.g., Korean and Thai). Based on the results from Tables 4 and 5, we employ the CPE method to produce the word2word dataset.

3.2 Word Segmentation

Since many of the 62 languages we consider are sensitive to word segmentation, we use language-specific tokenization tools when necessary. Specifically, we use publicly available tokenization packages for morphologically complex languages, i.e., Arabic [attia2007arabic] and Korean, and languages in which words are not separated by spaces, i.e., Chinese, Japanese, Thai, and Vietnamese999Spaces in Vietnamese delimit syllables.. For all other languages, we use the tok-tok tokenizer [dehdari2014neurophysiologically] implemented in NLTK [bird2009natural]. Table 6 summarizes the tokenization packages we used in the word2word dataset and their references.

4 The word2word Python Interface

As part of releasing the dataset and making it easily accessible and reproducible, we also introduce the word2word Python package. The open-source package provides an easy-to-use interface for both downloading and accessing bilingual lexicons for any of the 3,564 language pairs and building a custom bilingual lexicon on other language pairs for which there is a parallel corpus. Our source code is available on PyPi as https://pypi.org/project/word2word/.

4.1 Implementation

The word2word package is built entirely using Python 3. The package includes scripts for downloading and pre-processing parallel corpora from OpenSubtitles2018, including word segmentation, and for computing the CPE scores for all available word tokens within each parallel corpus. After processing, the package stores the bilingual lexicon as a Python pickle file, typically sized a few megabytes per language pair. The pickle file contains a Python dictionary that maps each source word to a list of top-10 word correspondences in time. This allows bilingual lexicons to be portable and accessible.

4.2 Usage

The Python interface provides a simple API to download and access the word2word dataset. As demonstrated in Figure 2, word translations for any query word can be retrieved as a list with a few lines of Python code.

[fontsize=]python from word2word import Word2word

en2fr = Word2word(’en’, ’fr’) print(en2fr(’apple’)) # [’pomme’, ’pommes’, ’pommier’, # ’tartes’, ’fleurs’]

Figure 2: The word2word Python interface for retrieving word translations.

4.3 Building a Custom Bilingual Lexicon

The word2word package also allows training a custom bilingual lexicon using a different parallel corpus. This can be useful in cases where there are larger and/or higher-quality parallel corpora available for the language pair of interest or when utilizing word translations for a particular domain (e.g., government, law, and medical). This process can also be done using a few lines of Python code, as demonstrated in Figure 3. For an OpenSubtitles2018 corpus of a million parallel sentences, building a bilingual lexicon takes approximately 10 minutes using 8 CPUs.

[fontsize=]python from word2word import Word2word

my_en2fr = Word2word.make( ’en’, ’fr’, ’data/pubmed.en-fr’ ) # …building…done! print(my_en2fr(’mitochondrial’)) # [’mitochondriale’, ’mitochondriales’, # ’mitochondrial’, ’cytopathies’, # ’mitochondriaux’]

Figure 3: The word2word Python interface for building a custom bilingual lexicon. Once built, the lexicon can be accessed in the same way as done in Figure 2.

5 Conclusion

In this paper, we present the word2word dataset, a publicly available collection of bilingual lexicons for 3,564 language pairs that are extracted from OpenSubtitles2018. The bilingual lexicons have high coverage (up to hundreds of thousands words) for many language pairs and provide word translations of similar or better quality compared to those from a state-of-the-art embedding model. We also release the word2word Python package, with which the user can easily access the dataset or build a custom lexicon for different parallel corpora. We hope that the dataset and its Python interface can facilitate research on improving cross-lingual models, including machine translation models [ramesh2018neural, gu2019pointer] and cross-lingual word embeddings [conneau2017word, ruder2017survey].

6 Bibliographical References


Appendix A Sample Translations from Different Extraction Methods

In Table 7, we compare the BLE methods described in Section 3.1 from illustrative examples of their extracted bilingual lexicons for English to Spanish and English to Simplified Chinese. These examples show that the CPE approach provides the correct correspondence as its top-1 translation in both languages, while the PMI approach seems to excessively favor rarer words among the co-occurrences. As illustrated in the English-Chinese example, this can be particularly problematic with languages such as Chinese, where word segmentation is highly nontrivial. The co-occurrence method prefers stop words that are frequent over the entire document, rather than the corresponding words.

a.1 Co-occurrences

The baseline co-occurrence model performs poorly in both experiments (Tables 4 and 5). As exemplified in Table 7, we find that the top-5 predictions in many cases are primarily stop words, such as la (the), de (of), and que (that) in Spanish and 的 (of), 你 (you), and 我 (I, me) in Chinese, because they frequently occur in any sentence, regardless of context.

a.2 Comparing PMI and CPE

Comparing translations using PMI and CPE, we find in Table 7 that PMI favors less frequent words excessively. This results in two kinds of error cases: (a) when PMI overemphasizes rare words in the target vocabulary, e.g. solarización for library in en-es, and (b) when PMI misses correct words in the target language that are relatively frequently used, e.g. bien for good in en-es. Another consequence is that PMI prefers less common variants of the same word, in particular conjugations and past/future tenses as well as typos, when two forms of the same word have comparable counts (e.g. obligados preferred over obligado in Spanish for the English obliged).

Because of the second reason, we also find that word2word tends to be more robust to tokenization issues, which are common in non-whitespace-separated languages like Chinese. For example, since the tokenizer failed to separate 张开嘴 (open mouth), which in general occurs far less frequently than 嘴 (mouth), PMI favors 张开嘴 over the more frequent 嘴 as its first choice.

Appendix B Full Dataset Statistics

In Table 8, we list the sizes of all 3,564 bilingual lexicons in the word2word dataset. By size, we refer to the number of source words for which translations exist. For each source word, we extract up to 10 (9+ on average) most likely translations according to the CPE method described in 3.1.3


af ar bg bn br bs ca cs da de el en eo es et eu fa fi fr gl he hi hr hu hy id is it ja ka kk ko lt lv mk ml ms nl no pl pt pt_br ro ru si sk sl sq sr sv ta te th tl tr uk ur vi ze_en ze_zh zh_cn zh_tw
af 0 8K 11K 2K 0 4K 0 11K 9K 11K 15K 19K 1K 16K 5K 0 4K 8K 11K 0 9K 1K 9K 11K 0 4K 0 10K 2K 0 0 0 972 1K 4K 3K 1K 12K 5K 10K 12K 16K 16K 10K 1K 5K 8K 1K 10K 6K 1K 0 2K 0 13K 3K 0 3K 1K 0 6K 3K
ar 18K 0 347K 158K 17K 337K 182K 342K 341K 342K 347K 335K 27K 343K 330K 236K 307K 353K 347K 70K 347K 58K 343K 350K 6K 319K 331K 346K 336K 102K 2K 336K 328K 197K 316K 159K 321K 364K 355K 353K 346K 347K 343K 332K 196K 348K 340K 330K 344K 351K 26K 21K 327K 12K 335K 241K 26K 309K 231K 302K 334K 326K
bg 20K 211K 0 98K 15K 206K 117K 204K 209K 201K 196K 188K 24K 196K 208K 162K 220K 211K 205K 45K 213K 36K 199K 202K 2K 211K 200K 205K 205K 73K 4K 211K 206K 133K 213K 98K 194K 210K 216K 209K 199K 196K 194K 207K 125K 209K 207K 196K 195K 215K 18K 16K 201K 10K 193K 161K 20K 206K 134K 188K 220K 223K
bn 2K 89K 86K 0 0 57K 12K 85K 79K 79K 88K 97K 0 89K 75K 24K 72K 82K 80K 8K 85K 10K 85K 86K 0 85K 23K 77K 52K 8K 0 36K 41K 23K 57K 30K 62K 82K 72K 87K 84K 90K 88K 77K 25K 48K 81K 44K 83K 82K 3K 0 48K 3K 87K 21K 6K 69K 11K 10K 78K 63K
br 0 9K 9K 0 0 4K 5K 10K 8K 9K 10K 10K 2K 10K 9K 1K 5K 9K 10K 1K 9K 0 9K 10K 0 7K 5K 8K 0 0 0 0 0 0 1K 2K 0 9K 8K 10K 10K 10K 10K 8K 0 5K 9K 5K 9K 7K 0 0 0 0 10K 2K 0 0 0 0 6K 0
bs 8K 276K 264K 84K 5K 0 93K 260K 279K 269K 259K 253K 13K 262K 266K 123K 275K 276K 267K 38K 272K 26K 261K 271K 200 265K 216K 276K 241K 65K 2K 155K 215K 122K 272K 85K 215K 279K 278K 277K 272K 262K 260K 274K 108K 273K 268K 261K 254K 280K 13K 13K 280K 5K 259K 122K 13K 274K 128K 131K 278K 283K
ca 0 82K 84K 11K 5K 52K 0 89K 68K 73K 87K 95K 0 96K 66K 19K 49K 77K 86K 12K 83K 5K 82K 88K 0 60K 18K 86K 40K 6K 0 21K 23K 10K 42K 7K 22K 86K 57K 89K 85K 95K 88K 74K 8K 41K 74K 24K 83K 69K 0 0 40K 0 90K 23K 0 46K 22K 21K 51K 33K
cs 21K 264K 262K 123K 18K 259K 149K 0 266K 258K 252K 245K 33K 250K 255K 208K 267K 268K 262K 65K 266K 44K 260K 258K 7K 256K 263K 270K 251K 86K 2K 256K 249K 167K 262K 124K 236K 266K 268K 260K 261K 256K 252K 262K 151K 255K 265K 247K 252K 265K 19K 18K 254K 11K 251K 215K 23K 252K 211K 253K 265K 272K
da 10K 164K 159K 68K 7K 167K 65K 156K 0 160K 157K 151K 9K 159K 161K 92K 155K 160K 162K 23K 166K 24K 159K 159K 0 157K 158K 163K 149K 44K 3K 126K 153K 90K 157K 59K 146K 162K 160K 161K 159K 161K 158K 166K 77K 162K 161K 150K 157K 160K 12K 12K 151K 3K 155K 86K 12K 151K 115K 118K 171K 166K
de 15K 155K 157K 72K 10K 163K 78K 153K 160K 0 143K 142K 18K 147K 162K 112K 172K 155K 152K 36K 162K 28K 151K 156K 4K 165K 163K 157K 166K 45K 4K 145K 161K 94K 168K 69K 157K 156K 161K 155K 157K 148K 143K 164K 80K 171K 158K 166K 148K 159K 12K 10K 171K 11K 145K 103K 14K 163K 105K 92K 167K 174K
el 25K 241K 236K 108K 14K 241K 119K 222K 248K 229K 0 197K 30K 211K 231K 178K 246K 255K 218K 66K 251K 38K 214K 222K 6K 237K 220K 228K 221K 71K 3K 220K 223K 143K 232K 113K 213K 237K 241K 226K 226K 216K 214K 246K 131K 234K 245K 215K 213K 261K 17K 15K 218K 12K 211K 171K 19K 225K 136K 184K 252K 251K
en 18K 98K 96K 60K 9K 101K 67K 95K 98K 94K 87K 0 22K 97K 103K 89K 107K 93K 91K 51K 104K 26K 89K 94K 4K 101K 98K 95K 102K 41K 3K 106K 98K 67K 104K 59K 93K 91K 95K 93K 89K 99K 89K 96K 73K 106K 102K 98K 86K 98K 14K 12K 109K 10K 86K 94K 14K 96K 112K 121K 102K 106K
eo 2K 20K 21K 0 2K 10K 0 27K 12K 22K 26K 38K 0 34K 18K 4K 8K 18K 23K 544 19K 2K 20K 24K 2K 13K 2K 23K 7K 0 1K 2K 3K 2K 2K 2K 4K 19K 12K 26K 22K 31K 27K 24K 3K 7K 20K 4K 26K 17K 0 0 8K 3K 28K 5K 0 4K 1K 4K 15K 9K
es 22K 144K 142K 77K 13K 143K 97K 137K 145K 138K 132K 131K 30K 0 149K 130K 151K 140K 134K 74K 150K 33K 133K 136K 6K 147K 142K 138K 148K 57K 3K 145K 141K 98K 145K 79K 140K 142K 144K 137K 136K 136K 132K 141K 96K 151K 147K 140K 130K 144K 18K 16K 151K 12K 134K 125K 17K 141K 141K 153K 149K 153K
et 8K 251K 257K 102K 15K 254K 102K 245K 260K 256K 259K 256K 21K 264K 0 143K 255K 258K 262K 38K 257K 32K 256K 257K 488 251K 249K 256K 250K 61K 2K 164K 251K 143K 247K 88K 238K 263K 265K 260K 258K 259K 257K 262K 122K 251K 253K 243K 254K 261K 13K 14K 243K 4K 256K 137K 19K 244K 142K 154K 266K 261K
eu 0 167K 175K 29K 2K 103K 24K 185K 137K 157K 185K 200K 5K 202K 129K 0 95K 172K 181K 14K 167K 12K 167K 182K 0 107K 35K 167K 66K 11K 0 43K 55K 29K 62K 29K 59K 176K 119K 183K 178K 198K 189K 141K 25K 90K 154K 37K 174K 160K 5K 1K 47K 3K 191K 31K 3K 81K 19K 19K 127K 110K
fa 5K 215K 222K 86K 7K 211K 62K 220K 216K 217K 226K 213K 9K 224K 210K 95K 0 221K 223K 25K 218K 27K 216K 217K 0 203K 99K 212K 213K 45K 2K 152K 156K 86K 207K 67K 207K 224K 214K 231K 216K 222K 220K 210K 93K 205K 220K 187K 220K 223K 12K 9K 194K 9K 219K 85K 17K 210K 52K 76K 208K 216K
fi 14K 338K 332K 136K 16K 339K 158K 329K 333K 331K 331K 319K 25K 328K 330K 236K 340K 0 339K 51K 340K 45K 331K 332K 3K 326K 329K 328K 334K 94K 5K 307K 323K 197K 327K 126K 313K 339K 331K 332K 334K 331K 328K 344K 172K 336K 335K 323K 329K 338K 21K 18K 334K 10K 327K 225K 22K 327K 256K 273K 342K 343K
fr 13K 120K 120K 62K 11K 121K 75K 114K 122K 115K 109K 108K 18K 112K 124K 99K 127K 117K 0 44K 127K 26K 110K 116K 2K 125K 120K 116K 124K 43K 2K 122K 123K 78K 122K 62K 120K 117K 121K 116K 115K 112K 109K 124K 72K 125K 124K 121K 108K 120K 12K 9K 122K 11K 110K 94K 14K 120K 101K 96K 126K 131K
gl 0 45K 41K 8K 2K 28K 14K 50K 30K 43K 54K 83K 648 87K 32K 13K 27K 37K 63K 0 34K 1K 43K 45K 0 23K 6K 58K 18K 3K 0 11K 14K 7K 21K 6K 12K 41K 25K 50K 47K 75K 50K 41K 6K 19K 37K 12K 52K 32K 0 0 19K 0 62K 8K 2K 21K 5K 4K 34K 22K
he 16K 250K 246K 109K 15K 236K 123K 240K 250K 248K 249K 239K 23K 251K 230K 166K 235K 253K 258K 40K 0 40K 248K 248K 3K 227K 230K 252K 223K 73K 4K 215K 219K 142K 242K 105K 215K 256K 258K 247K 245K 246K 244K 240K 127K 228K 245K 216K 247K 263K 22K 16K 222K 11K 241K 163K 20K 218K 135K 179K 254K 245K
hi 1K 24K 22K 8K 0 14K 4K 21K 19K 21K 21K 27K 1K 24K 18K 7K 18K 21K 21K 987 21K 0 21K 21K 0 17K 4K 19K 15K 1K 0 11K 12K 7K 10K 4K 13K 21K 16K 23K 18K 25K 24K 17K 8K 14K 18K 12K 22K 20K 12K 9K 13K 790 22K 4K 2K 14K 2K 805 19K 16K
hr 17K 252K 245K 119K 14K 249K 138K 242K 250K 237K 230K 226K 23K 230K 250K 179K 269K 251K 245K 57K 253K 45K 0 246K 5K 243K 246K 245K 282K 84K 3K 272K 243K 154K 246K 109K 242K 242K 249K 245K 232K 230K 236K 248K 153K 253K 247K 238K 233K 255K 21K 20K 261K 11K 235K 192K 22K 245K 199K 229K 260K 261K
hu 21K 365K 363K 157K 19K 360K 195K 356K 369K 363K 342K 331K 35K 348K 360K 274K 360K 379K 360K 71K 368K 50K 360K 0 4K 352K 358K 364K 350K 108K 4K 349K 343K 213K 351K 149K 327K 379K 374K 362K 365K 350K 347K 358K 200K 357K 366K 341K 343K 375K 21K 19K 345K 15K 339K 274K 25K 348K 291K 325K 373K 369K
hy 0 6K 3K 0 0 216 0 8K 0 6K 8K 8K 3K 8K 526 0 0 3K 3K 0 3K 0 5K 3K 0 455 0 826 0 0 0 0 0 0 520 154 0 3K 0 7K 6K 8K 8K 3K 0 202 585 425 8K 3K 0 0 0 0 8K 0 0 0 0 0 4K 3K
id 4K 132K 133K 60K 7K 134K 50K 130K 132K 131K 132K 126K 11K 133K 128K 63K 134K 132K 134K 17K 132K 20K 129K 131K 394 0 92K 130K 120K 29K 1K 88K 113K 65K 129K 48K 117K 134K 0 134K 132K 132K 130K 129K 65K 130K 130K 125K 129K 133K 10K 9K 129K 6K 130K 56K 13K 125K 68K 68K 134K 134K
is 0 251K 249K 30K 6K 197K 22K 245K 249K 247K 248K 236K 2K 245K 242K 35K 116K 252K 249K 5K 248K 6K 246K 243K 0 175K 0 247K 61K 16K 2K 33K 111K 60K 139K 24K 96K 252K 228K 250K 245K 247K 244K 212K 30K 142K 243K 121K 246K 255K 3K 0 75K 2K 243K 28K 2K 125K 25K 16K 177K 131K
it 14K 145K 149K 67K 11K 151K 84K 146K 147K 144K 139K 135K 20K 139K 149K 110K 156K 144K 142K 49K 148K 26K 142K 146K 787 151K 151K 0 138K 42K 3K 135K 146K 89K 156K 64K 138K 149K 150K 148K 147K 140K 140K 151K 78K 158K 145K 153K 138K 146K 10K 9K 139K 12K 138K 110K 14K 150K 111K 100K 153K 159K
ja 2K 77K 78K 28K 0 65K 24K 79K 75K 76K 78K 83K 7K 80K 75K 30K 72K 77K 79K 10K 76K 12K 77K 77K 0 78K 24K 79K 0 15K 4K 42K 39K 25K 58K 18K 52K 78K 76K 79K 76K 79K 78K 77K 28K 62K 77K 41K 78K 76K 7K 4K 60K 6K 78K 26K 6K 63K 18K 22K 77K 63K
ka 0 82K 87K 10K 0 61K 10K 83K 74K 66K 86K 92K 0 89K 65K 14K 59K 80K 77K 4K 81K 2K 81K 82K 0 54K 16K 61K 31K 0 0 20K 28K 10K 49K 8K 25K 84K 58K 85K 81K 87K 88K 63K 9K 28K 78K 33K 83K 73K 0 0 37K 2K 86K 13K 4K 42K 2K 7K 55K 35K
kk 0 2K 5K 0 0 3K 0 2K 5K 5K 4K 5K 2K 4K 2K 0 2K 5K 2K 0 4K 0 4K 4K 0 2K 2K 4K 2K 0 0 0 2K 2K 0 0 2K 3K 4K 6K 5K 4K 4K 4K 0 2K 4K 0 5K 5K 0 0 2K 0 4K 2K 0 2K 0 0 2K 0
ko 0 82K 82K 23K 0 56K 19K 81K 70K 73K 81K 87K 1K 83K 60K 22K 60K 79K 83K 10K 84K 10K 83K 82K 0 61K 18K 84K 45K 11K 0 0 31K 20K 40K 14K 41K 82K 60K 83K 83K 85K 84K 79K 20K 47K 73K 26K 81K 71K 6K 1K 40K 1K 83K 20K 6K 49K 14K 16K 58K 49K
lt 1K 312K 312K 69K 0 252K 37K 304K 311K 296K 309K 296K 3K 305K 306K 76K 226K 313K 311K 18K 307K 23K 306K 303K 0 269K 148K 303K 140K 31K 2K 93K 0 124K 203K 55K 165K 314K 270K 312K 304K 307K 303K 293K 70K 214K 308K 173K 307K 316K 15K 13K 136K 3K 301K 63K 7K 195K 31K 43K 269K 213K
lv 2K 165K 168K 33K 0 124K 14K 167K 157K 150K 166K 170K 2K 172K 156K 34K 104K 168K 163K 8K 167K 12K 166K 167K 0 137K 72K 155K 65K 12K 2K 43K 108K 0 93K 27K 81K 167K 131K 170K 169K 169K 171K 158K 30K 106K 157K 85K 165K 147K 5K 5K 67K 0 170K 31K 2K 100K 17K 19K 125K 106K
mk 6K 227K 226K 71K 2K 220K 54K 221K 225K 214K 228K 214K 2K 220K 218K 63K 216K 226K 222K 22K 222K 16K 217K 218K 514 210K 128K 213K 144K 42K 0 85K 141K 73K 0 62K 148K 231K 216K 230K 222K 225K 219K 214K 74K 206K 221K 169K 216K 228K 7K 3K 162K 6K 217K 69K 13K 195K 41K 57K 226K 199K
ml 5K 244K 223K 66K 3K 142K 10K 233K 177K 197K 241K 268K 3K 251K 162K 51K 148K 202K 222K 9K 210K 9K 210K 222K 129 162K 41K 185K 75K 12K 0 42K 73K 34K 120K 0 89K 202K 146K 224K 218K 254K 242K 180K 39K 98K 190K 77K 227K 199K 6K 0 77K 7K 246K 21K 2K 118K 9K 17K 185K 132K
ms 1K 117K 118K 40K 0 93K 16K 115K 113K 111K 116K 116K 3K 116K 110K 30K 110K 115K 114K 9K 113K 14K 114K 115K 0 112K 50K 111K 70K 15K 1K 49K 63K 37K 80K 26K 0 117K 101K 118K 114K 117K 117K 107K 43K 86K 113K 65K 115K 114K 6K 4K 69K 5K 115K 28K 10K 94K 28K 24K 114K 91K
nl 14K 130K 128K 60K 9K 135K 73K 125K 132K 128K 117K 116K 14K 119K 134K 102K 136K 130K 123K 28K 135K 24K 123K 128K 2K 134K 132K 131K 133K 43K 2K 133K 130K 82K 133K 56K 128K 0 132K 129K 125K 118K 117K 139K 77K 134K 132K 127K 120K 132K 12K 10K 131K 7K 122K 100K 13K 131K 129K 132K 139K 140K
no 5K 179K 174K 65K 8K 179K 58K 171K 171K 172K 166K 160K 10K 167K 179K 86K 173K 171K 171K 19K 180K 21K 167K 174K 0 0 154K 177K 161K 37K 3K 115K 148K 81K 171K 50K 155K 173K 0 176K 169K 168K 165K 177K 70K 174K 180K 153K 166K 173K 8K 9K 161K 3K 166K 74K 12K 171K 78K 78K 186K 180K
pl 20K 262K 258K 125K 18K 265K 157K 253K 266K 260K 247K 240K 34K 249K 257K 206K 267K 263K 260K 70K 266K 44K 259K 258K 7K 251K 264K 268K 262K 89K 5K 264K 260K 168K 257K 122K 249K 264K 268K 0 257K 249K 247K 261K 158K 265K 260K 256K 248K 266K 20K 18K 262K 12K 245K 212K 23K 253K 260K 278K 270K 269K
pt 18K 157K 154K 76K 12K 156K 88K 154K 156K 158K 150K 144K 19K 149K 152K 123K 155K 157K 154K 43K 156K 27K 148K 157K 4K 154K 151K 158K 146K 54K 4K 147K 147K 101K 153K 75K 142K 156K 157K 157K 0 147K 148K 158K 92K 156K 154K 143K 149K 161K 14K 14K 145K 10K 146K 113K 17K 148K 129K 142K 160K 162K
pt_br 20K 137K 135K 69K 11K 137K 84K 131K 139K 130K 126K 125K 25K 128K 136K 119K 137K 135K 129K 58K 143K 30K 124K 130K 6K 133K 131K 131K 128K 50K 3K 128K 127K 88K 138K 73K 125K 132K 137K 130K 129K 0 125K 133K 85K 137K 137K 126K 126K 140K 15K 12K 128K 11K 127K 112K 15K 130K 126K 142K 140K 140K
ro 24K 184K 177K 90K 12K 178K 107K 169K 182K 169K 168K 164K 28K 170K 180K 150K 180K 179K 174K 48K 185K 36K 166K 169K 7K 176K 170K 175K 169K 65K 3K 169K 164K 113K 179K 92K 166K 171K 178K 172K 168K 172K 0 186K 112K 177K 180K 157K 169K 185K 19K 17K 167K 11K 165K 145K 18K 171K 168K 179K 185K 183K
ru 21K 296K 288K 133K 15K 292K 133K 286K 310K 292K 282K 249K 32K 260K 297K 186K 300K 308K 286K 56K 290K 35K 287K 283K 3K 293K 249K 291K 289K 74K 4K 268K 274K 166K 297K 111K 270K 308K 307K 298K 291K 270K 282K 0 148K 297K 292K 288K 286K 306K 13K 13K 285K 15K 285K 214K 21K 290K 137K 171K 309K 325K
si 2K 160K 160K 32K 0 100K 10K 155K 136K 125K 159K 182K 3K 165K 128K 25K 111K 149K 144K 7K 151K 12K 149K 157K 0 129K 32K 134K 76K 9K 0 38K 57K 26K 85K 25K 87K 152K 107K 163K 153K 166K 165K 131K 0 68K 142K 57K 157K 145K 7K 3K 59K 4K 162K 18K 6K 91K 12K 15K 115K 88K
sk 9K 278K 279K 68K 7K 276K 65K 268K 285K 286K 286K 270K 9K 277K 275K 105K 265K 283K 290K 24K 285K 27K 275K 276K 195 265K 165K 288K 192K 33K 2K 130K 186K 107K 262K 63K 188K 288K 286K 289K 279K 280K 273K 289K 75K 0 280K 200K 277K 284K 11K 10K 249K 2K 279K 101K 13K 258K 98K 97K 289K 274K
sl 14K 223K 220K 102K 14K 224K 106K 225K 226K 223K 223K 220K 23K 225K 223K 148K 227K 229K 229K 42K 227K 30K 222K 226K 541 220K 216K 224K 223K 68K 3K 186K 214K 130K 220K 90K 202K 232K 228K 230K 221K 227K 221K 222K 117K 228K 0 212K 221K 228K 15K 14K 218K 10K 222K 146K 19K 215K 152K 171K 232K 228K
sq 2K 218K 214K 50K 7K 204K 26K 210K 210K 205K 215K 204K 5K 211K 210K 33K 175K 218K 212K 12K 214K 20K 210K 209K 399 196K 108K 210K 94K 30K 0 46K 114K 64K 167K 39K 112K 216K 186K 216K 209K 214K 209K 204K 48K 154K 209K 0 210K 213K 11K 10K 115K 7K 208K 44K 9K 149K 38K 43K 207K 145K
sr 20K 302K 289K 153K 14K 279K 171K 281K 302K 274K 266K 251K 36K 261K 306K 231K 320K 298K 264K 79K 319K 56K 268K 269K 7K 295K 289K 272K 344K 108K 6K 331K 308K 192K 311K 148K 308K 288K 300K 282K 279K 261K 271K 293K 200K 288K 298K 295K 0 306K 24K 22K 310K 11K 270K 237K 26K 299K 220K 263K 309K 319K
sv 7K 168K 167K 73K 7K 175K 68K 163K 168K 167K 163K 158K 13K 161K 172K 113K 170K 170K 165K 24K 176K 26K 165K 168K 2K 171K 171K 169K 167K 46K 3K 144K 167K 89K 173K 67K 159K 172K 171K 167K 167K 163K 163K 177K 89K 175K 168K 165K 164K 0 11K 10K 166K 6K 164K 95K 14K 166K 122K 122K 181K 178K
ta 2K 20K 17K 4K 0 11K 0 15K 14K 17K 15K 21K 0 17K 12K 5K 12K 19K 18K 0 20K 19K 16K 16K 0 13K 3K 11K 15K 0 0 11K 12K 5K 7K 5K 9K 19K 11K 16K 15K 20K 20K 12K 7K 10K 13K 11K 16K 13K 0 11K 12K 0 16K 0 0 11K 3K 887 19K 17K
te 0 13K 13K 0 0 10K 0 12K 13K 10K 13K 15K 0 14K 11K 1K 8K 13K 10K 0 13K 12K 13K 12K 0 10K 0 9K 6K 0 0 1K 9K 4K 3K 0 5K 13K 10K 13K 13K 13K 14K 9K 3K 8K 12K 9K 13K 11K 12K 0 2K 0 13K 0 0 8K 1K 0 12K 9K
th 3K 154K 162K 40K 0 168K 41K 159K 163K 165K 161K 156K 7K 157K 159K 34K 159K 164K 164K 14K 162K 17K 162K 158K 0 161K 54K 161K 119K 24K 1K 62K 76K 40K 131K 30K 88K 163K 164K 165K 159K 161K 158K 162K 39K 158K 161K 92K 161K 168K 9K 2K 0 7K 156K 48K 8K 131K 35K 43K 165K 123K
tl 0 8K 8K 2K 0 3K 0 8K 3K 11K 10K 13K 3K 11K 3K 3K 8K 7K 11K 0 8K 846 8K 10K 0 7K 2K 11K 10K 2K 0 2K 2K 0 5K 4K 7K 9K 4K 8K 10K 11K 9K 11K 4K 2K 7K 6K 7K 7K 0 0 8K 0 12K 2K 0 7K 0 0 8K 2K
tr 28K 303K 300K 147K 20K 298K 182K 296K 307K 296K 290K 280K 41K 292K 296K 252K 304K 311K 301K 98K 308K 50K 292K 293K 8K 297K 294K 299K 299K 105K 4K 297K 287K 192K 296K 158K 280K 306K 304K 298K 294K 295K 290K 306K 190K 308K 302K 287K 291K 315K 22K 20K 295K 19K 0 249K 27K 294K 279K 306K 310K 305K
uk 5K 271K 286K 36K 3K 159K 45K 301K 212K 216K 288K 316K 7K 310K 200K 42K 148K 264K 268K 12K 263K 8K 267K 285K 0 158K 37K 270K 103K 15K 2K 51K 67K 36K 114K 18K 76K 277K 161K 295K 266K 306K 297K 263K 25K 133K 239K 78K 275K 214K 0 0 98K 3K 291K 0 3K 119K 30K 28K 169K 114K
ur 0 14K 13K 6K 0 7K 0 13K 12K 12K 13K 14K 0 14K 13K 3K 11K 13K