Varnam has a tokenizer that converts Malayalam (or any other Indian lang) text to manglish patterns. While learning, Varnam makes a database of such patterns -> word :
Pattern | Word ID | Learned
"mal" "77156" "0"
"mala" "228" "1"
"mala" "1586" "1"
"mala" "5434" "1"
"mala" "50134" "1"
"mala" "57521" "0"
"malaa" "50134" "1"
"malaa" "57521" "0"
"malaagha" "7784" "1"
"malaaghama" "82823" "0"
"malaaghamaa" "82823" "0"
"malaaghamaar" "25013" "1"
"malaaghamar" "25013" "1"
"malaak" "102229" "1"
"malaaka" "24048" "1"
"malaaka" "43013" "1"
This makes the database huge in size. Varnam makes malayalam suggestions from this database (the learnings database) looking up pattern. If it can't find one, uses the tokenizer to make word.
I want to know why this approach wasn't chosen @navaneeth :
- No need of a
pattern => word DB (learnings file). Instead, just need a word dictionary.
- Add more patterns to VST (Varnam Symbol Table). Prioritized letters
n => ന, ണ. Capitalized N will always give ണ. So pani will give suggestions in priority : പനി, പണി. Currently if only the learnings DB has pani assigned to both words will give the different outputs.
- When an input say
pani is given to varnam, it should tokenize to പനി and പണി using just VST, and then look up the word dictionary to find words starting with പനി and പണി and give additional suggestions.
- For english words like "Cricket", the tokenization will give bad results, in such cases we can maybe use a
pattern => word DB like the current learnings DB.
By doing so, the size of the learnings database can be reduced a lot.
Varnam has a tokenizer that converts Malayalam (or any other Indian lang) text to manglish patterns. While learning, Varnam makes a database of such patterns -> word :
This makes the database huge in size. Varnam makes malayalam suggestions from this database (the learnings database) looking up pattern. If it can't find one, uses the tokenizer to make word.
I want to know why this approach wasn't chosen @navaneeth :
pattern => wordDB (learnings file). Instead, just need a word dictionary.n => ന, ണ. CapitalizedNwill always giveണ. Sopaniwill give suggestions in priority :പനി, പണി. Currently if only the learnings DB haspaniassigned to both words will give the different outputs.paniis given to varnam, it should tokenize toപനിandപണിusing just VST, and then look up the word dictionary to find words starting withപനിandപണിand give additional suggestions.pattern => wordDB like the current learnings DB.By doing so, the size of the learnings database can be reduced a lot.