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- 2011 10K
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- 2013 10K
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- 2013 100K
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- 2015 10K
- 2015 30K
- 2015 100K
- 2015 300K
- 2016 10K
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- 2016 100K
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- 2017 100K
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- 2018 10K
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- 1998 100K
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- 2007 30K
- 2007 100K
- 2007 300K
- 2008 10K
- 2008 30K
- 2008 100K
- 2008 300K
- 2009 10K
- 2009 30K
- 2009 100K
- 2009 300K
- 2010 10K
- 2010 30K
- 2010 100K
- 2010 300K
- 2010 1M
- 2011 10K
- 2011 30K
- 2011 100K
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- 2012 10K
- 2012 30K
- 2012 100K
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- 2018 10K
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- 2012 10K
- 2012 30K
- 2012 100K
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- 2019 10K
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- 2020 10K
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- 2007 10K
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- 2007 100K
- 2007 300K
- 2008 10K
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- 2008 100K
- 2008 300K
- 2010 10K
- 2010 30K
- 2010 100K
- 2010 300K
- 2011 10K
- 2011 30K
- 2011 100K
- 2011 300K
- 2012 10K
- 2012 100K
- 2012 1M
- 2019 10K
- 2019 30K
- 2019 100K
- 2019 300K
- 2020 10K
- 2020 30K
- 2020 100K
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- 2021 10K
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News
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News
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- 2008 10K
- 2008 30K
- 2008 100K
- 2008 300K
- 2009 10K
- 2009 30K
- 2009 100K
- 2009 300K
- 2010 10K
- 2010 30K
- 2010 100K
- 2010 300K
- 2011 10K
- 2011 30K
- 2011 100K
- 2011 300K
- 2019 10K
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News
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News
- 2007 10K
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- 2007 100K
- 2007 300K
- 2008 10K
- 2008 30K
- 2008 100K
- 2008 300K
- 2009 10K
- 2009 30K
- 2009 100K
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- 2010 10K
- 2010 30K
- 2010 100K
- 2010 300K
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- 2011 30K
- 2011 100K
- 2011 300K
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-
Modern Greek
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News
- 2007 10K
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- 2013 10K
- 2013 30K
- 2013 100K
- 2013 300K
- 2016 10K
- 2016 30K
- 2016 100K
- 2016 300K
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- 2017 30K
- 2017 100K
- 2017 300K
- 2018 10K
- 2018 30K
- 2018 100K
- 2018 300K
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- 2019 30K
- 2019 100K
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- 2020 10K
- 2020 30K
- 2020 100K
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News
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News
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- 2008 100K
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- 2011 100K
- 2011 300K
- 2011 1M
- 2013 10K
- 2013 30K
- 2013 100K
- 2013 300K
- 2016 10K
- 2016 30K
- 2016 100K
- 2016 300K
- 2017 10K
- 2017 30K
- 2017 100K
- 2017 300K
- 2018 10K
- 2018 30K
- 2018 100K
- 2018 300K
- 2019 10K
- 2019 30K
- 2019 100K
- 2019 300K
- 2020 10K
- 2020 30K
- 2020 100K
- 2020 300K
- 2021 10K
- 2021 30K
- 2021 100K
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News
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- 2009 100K
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- 2010 100K
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- 2013 10K
- 2013 30K
- 2013 100K
- 2013 300K
- 2013 1M
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- 2019 100K
- 2019 300K
- 2020 10K
- 2020 30K
- 2020 100K
- 2020 300K
- 2021 10K
- 2021 30K
- 2021 100K
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Spanish
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- 2001-2002 10K
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- 2007 100K
- 2007 300K
- 2008 10K
- 2008 30K
- 2008 100K
- 2008 300K
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- 2009 30K
- 2009 100K
- 2009 300K
- 2010 10K
- 2010 30K
- 2010 100K
- 2010 300K
- 2011 10K
- 2011 30K
- 2011 100K
- 2011 300K
- 2011 1M
- 2012 10K
- 2012 30K
- 2012 100K
- 2012 300K
- 2013 10K
- 2013 30K
- 2013 100K
- 2013 300K
- 2016 10K
- 2016 30K
- 2016 100K
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- 2017 100K
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- 2018 30K
- 2018 100K
- 2018 300K
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- 2019 30K
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- 2020 30K
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- 2021 30K
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- 2022 30K
- 2022 100K
- 2022 300K
- 2023 10K
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- Newscrawl
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News
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Swedish
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News
- 2005 10K
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- 2006 10K
- 2006 30K
- 2006 100K
- 2006 300K
- 2007 10K
- 2007 30K
- 2007 100K
- 2007 300K
- 2012 10K
- 2012 30K
- 2012 100K
- 2012 300K
- 2019 10K
- 2019 30K
- 2019 100K
- 2019 300K
- 2020 10K
- 2020 30K
- 2020 100K
- 2020 300K
- 2022 10K
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- 2023 10K
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News
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- Western Frisian
- Western Mari
- Western Panjabi
- Wu Chinese
- Xhosa
- Yakut
- Yiddish
- Yoruba
- Yue Chinese
- Zeeuws
- Zulu
NN Co-occurrences of the Top-1000 Words
NN Co-occurrences of the Top-10 Words
| Left Nb. | Right Nb. | Frequency |
|---|---|---|
| fan | De | 656 |
| fan | in | 1932 |
| fan | it | 4347 |
| fan | de | 10520 |
| op | in | 897 |
| op | it | 2594 |
| op | de | 4624 |
| yn | in | 1468 |
| yn | it | 3428 |
| yn | de | 7354 |
| en | is | 319 |
| en | it | 739 |
| en | in | 771 |
| en | de | 1932 |
| is | yn | 571 |
| is | de | 1359 |
| is | it | 1114 |
| is | in | 1218 |
256 msec needed at 2019-11-06 08:03
5.1.6 Language Fingerprint
Data Description
NN co-occurrences within the 10 most frequent words are presented in a table.
The graph below gives much more information. Here, the top-1000 words are plotted against each other and the dots indicate NN co-occurrences. The diameter of the dots increases with the significance of the co-occurrence. Both axis are scaled logarithmic to shift the emphasis to the top words.
Usage
The picture above is very typical for a language, therefore the name language fingerprint. Comparing these fingerprints for different languages one is able to identify determiners, prepositions etc. by its graphical properties.
Simplified Select statement
Frequency of the most frequent word:
select @maxfreq:=(select freq from words where w_id=101);
Table data:
select w1.word,w2.word,c.freq from co_n c, words w1, words w2 where w1.w_id=w1_id and w2.w_id=w2_id and w1_id>100 and w2_id>100 and 110>=w1_id and 110>=w2_id and c.freq>(select count(*) from sentences)/100000 order by w1.w_id;
Picture data:
select if(12>w1_id-99,w1.word,"-"),if(12>w2_id-99,w2.word,"-"),w1_id-99,w2_id-99,1/(log(c.freq/@maxfreq)*log(c.freq/@maxfreq)/20) from co_n c, words w1, words w2 where w1.w_id=w1_id and w2.w_id=w2_id and w1_id>100 and w2_id>100 and 1100>=w1_id and 1100>=w2_id and c.freq>(select count(*) from sentences)/100000;