28-09-2017, 10:22 AM
I have run the Python script on an English text (A Christmas Carol, by Charles Dickens).
Vowels and consonant are still nicely separated in the runs when the optimization performs well. In some runs, they aren't.
The additional difficulty is of course the presence of several consonant digraphs. Also the results illustrated in the attachments are almost “confused” about 'c' which is generated with 2.2% probability by the consonant state and 1.8% probability by the vowel state.
For instance, in these cases 'c' appears between two consonants, a position typical of vowels:
watched
kitchens
exclaimed
included
church
scrooge
What this algorithm does is trying to predict which character will occur given the class of the preceding character. Setting the model to only two states basically implies that there only are two classes of characters. These tend to map to consonants and vowels because consonants and vowels tend to alternate. State transition probabilities make this clear: the loops mapping state 0 to itself and state 1 to itself have much smaller probabilities than moving from 0 to 1 and from 1 to 0. The loops actually represent consonant-consonant and vowel-vowel sequences.
Vowels and consonant are still nicely separated in the runs when the optimization performs well. In some runs, they aren't.
The additional difficulty is of course the presence of several consonant digraphs. Also the results illustrated in the attachments are almost “confused” about 'c' which is generated with 2.2% probability by the consonant state and 1.8% probability by the vowel state.
For instance, in these cases 'c' appears between two consonants, a position typical of vowels:
watched
kitchens
exclaimed
included
church
scrooge
What this algorithm does is trying to predict which character will occur given the class of the preceding character. Setting the model to only two states basically implies that there only are two classes of characters. These tend to map to consonants and vowels because consonants and vowels tend to alternate. State transition probabilities make this clear: the loops mapping state 0 to itself and state 1 to itself have much smaller probabilities than moving from 0 to 1 and from 1 to 0. The loops actually represent consonant-consonant and vowel-vowel sequences.