Left Nb. | Right Nb. | Frequency |
---|---|---|
que | a | 3243 |
que | o | 3961 |
que | não | 3056 |
e | da | 578 |
e | que | 1516 |
e | não | 1255 |
o | que | 3311 |
do | que | 1807 |
para | que | 690 |
para | não | 248 |
para | a | 2329 |
para | o | 2602 |
em | que | 1517 |
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;