Spell Correctors¶
This package supports the use of spell correctors, because typos are very common in relatively short text data.
There are two types of spell correctors provided: the one described by Peter Norvig (using n-grams Bayesian method), and another by Keisuke Sakaguchi and his colleagues (using semi-character level recurrent neural network).
>>> import shorttext
We use the Norvig’s training corpus as an example. To load it, in Python 2.7, enter
>>> from urllib2 import urlopen
>>> text = urlopen('https://norvig.com/big.txt').read()
Or in Python 3.5 and 3.6,
>>> from urllib.request import urlopen
>>> text = urlopen('https://norvig.com/big.txt').read()
The developer just has to instantiate the spell corrector, and then train it with a corpus to get a correction model. Then one can use it for correction.
Norvig¶
Peter Norvig described a spell corrector based on Bayesian approach and edit distance. You can refer to his blog for more information.
>>> norvig_corrector = shorttext.spell.NorvigSpellCorrector()
>>> norvig_corrector.train(text)
>>> norvig_corrector.correct('oranhe') # gives "orange"
Sakaguchi (SCRNN - semi-character recurrent neural network)¶
Keisuke Sakaguchi and his colleagues developed this spell corrector with the insight that most of the typos happen in between the spellings. They developed a recurrent neural network that trains possible change within the spellings. There are six modes:
- JUMBLE-WHOLE
- JUMBLE-BEG
- JUMBLE-END
- JUMBLE-INT
- NOISE-INSERT
- NOISE-DELETE
- NOISE-REPLACE
The original intent of their work was not to invent a new spell corrector but to study the “Cmabrigde Uinervtisy” effect, but it is nice to see how it can be implemented as a spell corrector.
>>> scrnn_corrector = shorttext.spell.SCRNNSpellCorrector('JUMBLE-WHOLE')
>>> scrnn_corrector.train(text)
>>> scrnn_corrector.correct('oranhe') # gives "orange"
We can persist the SCRNN corrector for future use:
>>> scrnn_corrector.save_compact_model('/path/to/spellscrnn.bin')
To load,
>>> corrector = shorttext.spell.loadSCRNNSpellCorrector('/path/to/spellscrnn.bin')