Left Nb. | Right Nb. | Frequency |
---|---|---|
on | ta | 25 |
on | ka | 66 |
on | see | 115 |
ja | kui | 36 |
ja | ta | 22 |
ja | see | 36 |
et | kui | 63 |
et | ta | 69 |
et | see | 86 |
ka | see | 15 |
kui | see | 31 |
kui | ta | 35 |
kui | ka | 122 |
see | oli | 23 |
see | ei | 39 |
see | on | 99 |
ning | ta | 7 |
ning | see | 13 |
oli | ta | 7 |
oli | ka | 15 |
oli | see | 21 |
ta | oli | 8 |
ta | on | 35 |
ta | ei | 35 |
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;