Left Nb. | Right Nb. | Frequency |
---|---|---|
է | որ | 32 |
է | էլ | 4 |
է | թե | 36 |
է | ու | 237 |
է | եւ | 477 |
որ | է | 7 |
որ | որ | 3 |
որ | եւ | 4 |
որ | էլ | 11 |
որ | թե | 21 |
որ | մի | 132 |
որ | այդ | 541 |
են | որ | 5 |
են | թե | 12 |
են | եւ | 197 |
են | ու | 127 |
են | այդ | 187 |
եւ | որ | 37 |
եւ | էլ | 3 |
եւ | թե | 32 |
եւ | այդ | 197 |
ու | որ | 5 |
ու | էլ | 7 |
ու | այդ | 36 |
ու | մի | 85 |
էլ | եւ | 4 |
էլ | որ | 35 |
էլ | ու | 7 |
էլ | է | 212 |
էլ | թե | 9 |
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;