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NN Co-occurrences of the Top-1000 Words
NN Co-occurrences of the Top-10 Words
| Left Nb. | Right Nb. | Frequency |
|---|---|---|
| eta | ez | 699 |
| da | eta | 918 |
| ez | dira | 575 |
| ez | du | 909 |
| ez | da | 1826 |
| ere | izan | 79 |
| ere | ez | 327 |
| dira | eta | 501 |
| bat | edo | 122 |
| bat | izan | 160 |
| bat | ere | 216 |
| bat | da | 357 |
| izan | ez | 82 |
| izan | du | 257 |
| izan | ere | 331 |
| izan | dira | 371 |
| izan | behar | 468 |
| izan | da | 869 |
| edo | ez | 88 |
| behar | izan | 193 |
| behar | du | 407 |
| behar | dira | 490 |
| behar | da | 1036 |
140 msec needed at 2018-04-20 09:33
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;