Left Nb. | Right Nb. | Frequency |
---|---|---|
ба | он | 188 |
ба | ин | 503 |
дар | он | 283 |
дар | ин | 815 |
ва | дар | 398 |
ва | бо | 167 |
ки | он | 107 |
ки | ба | 553 |
ки | бо | 299 |
ки | ин | 402 |
ки | аз | 527 |
ки | дар | 1309 |
аз | ин | 504 |
аз | он | 326 |
ин | аст | 105 |
бо | он | 51 |
бо | ин | 148 |
аст | ва | 230 |
Дар | он | 42 |
Дар | ин | 435 |
он | дар | 96 |
он | ба | 104 |
он | ки | 149 |
он | аст | 96 |
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.
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.
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;