Automatic text normalisation
Revision as of 13:09, 23 March 2014 by Francis Tyers (talk | contribs)
General ideas
- Diacritic restoration
- Reduplicated character reduction
- How to learn language specific settings? -- e.g. in English certain consonants can double, but others cannot, same goes for vowels. Can we learn these by looking at a corpus ?
Code switching
- For the language subpart... we can actually train and keep copies of most frequently corrected words across languages and then refer to that list...
- Maybe this will be too heavy for the on the run application ( needs discussion )
- Is it possible to identify sub-spans of text ? e.g.
- LOL rte showin dáil in irish 4 seachtan na gaeilge, an ceann comhairle hasnt a scooby wots bein sed! his face is classic ha!
- [en LOL rte showin dáil in irish 4] [ga seachtan na gaeilge, an ceann comhairle] [en hasnt a scooby wots bein sed! his face is classic ha!]
To do list
- Feed charlifter with n-grams ( works best with a trigram model ). This would improve the diacritics at the moment
- Make list of most frequently occurring non dictionary words, these might be abbreviations..
- add most frequently occurring english abbreviations to the list
