Spelling Correction

Note

This tutorial is available as an IPython notebook here.

%%time
import malaya
CPU times: user 3.48 s, sys: 539 ms, total: 4.02 s
Wall time: 3.4 s
# some text examples copied from Twitter

string1 = 'krajaan patut bagi pencen awal skt kpd warga emas supaya emosi'
string2 = 'Husein ska mkn aym dkat kampng Jawa'
string3 = 'Melayu malas ni narration dia sama je macam men are trash. True to some, false to some.'
string4 = 'Tapi tak pikir ke bahaya perpetuate myths camtu. Nanti kalau ada hiring discrimination despite your good qualifications because of your race tau pulak marah. Your kids will be victims of that too.'
string5 = 'DrM cerita Melayu malas semenjak saya kat University (early 1980s) and now as i am edging towards retirement in 4-5 years time after a career of being an Engineer, Project Manager, General Manager'
string6 = 'blh bntg dlm kls nlp sy, nnti intch'

Load probability speller

The probability speller extends the functionality of the Peter Norvig’s, http://norvig.com/spell-correct.html.

And improve it using some algorithms from Normalization of noisy texts in Malaysian online reviews, https://www.researchgate.net/publication/287050449_Normalization_of_noisy_texts_in_Malaysian_online_reviews.

Also added custom vowels and consonant augmentation to adapt with our local shortform / typos.

prob_corrector = malaya.spell.probability()

To correct a word

prob_corrector.correct('sy')
'saya'
prob_corrector.correct('mhthir')
'mahathir'
prob_corrector.correct('mknn')
'makanan'

List possible generated pool of words

prob_corrector.edit_candidates('mhthir')
{'mahathir'}
prob_corrector.edit_candidates('smbng')
{'sambang',
 'sambong',
 'sambung',
 'sembang',
 'sembong',
 'sembung',
 'simbang',
 'smbg',
 'sombong',
 'sumbang',
 'sumbing'}

Now you can see, edit_candidates suggested quite a lot candidates and some of candidates not an actual word like sambang, to reduce that, we can use sentencepiece to check a candidate a legit word for malaysia context or not.

prob_corrector_sp = malaya.spell.probability(sentence_piece = True)
prob_corrector_sp.edit_candidates('smbng')
{'sambong',
 'sambung',
 'sembang',
 'sembong',
 'sembung',
 'smbg',
 'sombong',
 'sumbang',
 'sumbing'}

So how does the model knows which words need to pick? highest counts from wikipedia!

To correct a sentence

prob_corrector.correct_text(string1)
'kerajaan patut bagi pencen awal sakit kepada warga emas supaya emosi'
prob_corrector.correct_text(string2)
'Husein suka makan ayam dekat kampung Jawa'
prob_corrector.correct_text(string3)
'Melayu malas ni narration dia sama sahaja macam men are trash. True to some, false to some.'
prob_corrector.correct_text(string4)
'Tapi tak fikir ke bahaya perpetuate myths macam itu. Nanti kalau ada hiring discrimination despite your good qualifications because of your race tahu pula marah. Your kids will be victims of that too.'
prob_corrector.correct_text(string5)
'DrM cerita Melayu malas semenjak saya kat University (early 1980s) and now as saya am edging towards retirement in 4-5 years time after a career of being an Engineer, Project Manager, General Manager'
prob_corrector.correct_text(string6)
'boleh bintang dalam kelas nlp saya, nanti intch'

Load transformer speller

This spelling correction is a transformer based, improvement version of malaya.spell.probability. Problem with malaya.spell.probability, it naively picked highest probability of word based on public sentences (wiki, news and social media) without understand actual context, example,

string = 'krajaan patut bagi pencen awal skt kpd warga emas supaya emosi'
prob_corrector = malaya.spell.probability()
prob_corrector.correct_text(string)
-> 'kerajaan patut bagi pencen awal sakit kepada warga emas supaya emosi'

It supposely replaced skt with sikit, a common word people use in social media to give a little bit of attention to pencen. So, to fix that, we can use Transformer model! Right now transformer speller supported ``BERT`` and ``ALBERT`` only, XLNET is not that good.

model = malaya.transformer.load(model = 'bert', size = 'small')
transformer_corrector = malaya.spell.transformer(model, sentence_piece = True)
WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/bert/modeling.py:93: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/_transformer/_bert.py:48: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/bert/modeling.py:171: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/bert/modeling.py:409: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/bert/modeling.py:490: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.

WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/bert/modeling.py:671: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.Dense instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use layer.__call__ method instead.
WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/_transformer/_bert.py:85: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead.

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/_transformer/_bert.py:86: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/_transformer/_bert.py:87: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/_transformer/_bert.py:88: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/_transformer/_bert.py:93: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/_transformer/_bert.py:95: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

INFO:tensorflow:Restoring parameters from /Users/huseinzolkepli/Malaya/bert-model/small/bert-small-v2/model.ckpt
transformer_corrector.correct_text(string1)
'kerajaan patut bagi pencen awal sikit kepada warga emas supaya emosi'

perfect! But again, transformer model is very expensive! You can compare the time wall with probability based.

%%time
transformer_corrector.correct_text(string1)
CPU times: user 28.7 s, sys: 1.5 s, total: 30.2 s
Wall time: 6.11 s
'kerajaan patut bagi pencen awal sikit kepada warga emas supaya emosi'
%%time
prob_corrector.correct_text(string1)
CPU times: user 105 ms, sys: 7.19 ms, total: 112 ms
Wall time: 112 ms
'kerajaan patut bagi pencen awal sakit kepada warga emas supaya emosi'
transformer_corrector.correct_text(string2)
'Husein suke makan ayam dekat kampung Jawa'

Transformer did a mistake suggested suke instead suka, this is because Malaya Transformer trained more on local context (social media) instead of standard context.

Load symspeller speller

This spelling correction is an improvement version for symspeller to adapt with our local shortform / typos. Before you able to use this spelling correction, you need to install symspeller,

pip install symspellpy
symspell_corrector = malaya.spell.symspell()

To correct a word

symspell_corrector.correct('bntng')
'bintang'
symspell_corrector.correct('kerajaan')
'kerajaan'
symspell_corrector.correct('mknn')
'makanan'

List possible generated words

symspell_corrector.edit_step('mrh')
{'marah': 12684.0,
 'merah': 21448.5,
 'arah': 15066.5,
 'darah': 10003.0,
 'mara': 7504.5,
 'malah': 7450.0,
 'zarah': 3753.5,
 'murah': 3575.5,
 'barah': 2707.5,
 'march': 2540.5,
 'martha': 390.0,
 'marsha': 389.0,
 'maratha': 88.5,
 'marcha': 22.5,
 'karaha': 13.5,
 'maraba': 13.5,
 'varaha': 11.5,
 'marana': 4.5,
 'marama': 4.5}

To correct a sentence

symspell_corrector.correct_text(string1)
'kerajaan patut bagi pencen awal saat kepada warga emas supaya emosi'
symspell_corrector.correct_text(string2)
'Husein sama makan ayam dapat kampung Jawa'
symspell_corrector.correct_text(string3)
'Melayu malas ni narration dia sama sahaja macam men are trash. True to some, false to some.'
symspell_corrector.correct_text(string4)
'Tapi tak fikir ke bahaya perpetuate maathai macam itu. Nanti kalau ada hiring discrimination despite your good qualifications because of your race tahu pula marah. Your kids will be victims of that too.'
symspell_corrector.correct_text(string5)
'DrM cerita Melayu malas semenjak saya kat University (early 1980s) and now as saya am edging towards retirement in 4-5 aras time after a career of being an Engineer, Project Manager, General Manager'
symspell_corrector.correct_text(string6)
'ialah bintang dalam kelas malaya saya, nanti mintalah'