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| id | title | date | author | aliases | permalink | medium_post | mf2_mp-syndicate-to | mf2_syndication | tags | ||||
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| 2250 | Identifying Misspelled Words in your Dataset with Hunspell | 2018-01-22T05:17:16+00:00 | Brandon Rozek |
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/2018/01/identifying-misspelled-words-dataset-hunspell/ |
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This article is based on one written by Markus Konrad at this link https://datascience.blog.wzb.eu/2016/07/13/autocorrecting-misspelled-words-in-python-using-hunspell/
I assume in this article that you have hunspell and it's integration with python installed. If not, please refer to the article mention above and follow the prerequisite steps.
This article is inspired from the need to correct misspelled words in the Dress Attributes Dataset. I'll share with you my initial pitfall, and what I ended up doing instead.
Background Information
Misspelled words are common when dealing with survey data or data where humans type in the responses manually. In the Dress Attributes Dataset this is apparent when looking at the sleeve lengths of the different dresses.
dresses_data['SleeveLength'].value_counts()
| Word | Frequency |
|---|---|
| sleevless | 223 |
| full | 97 |
| short | 96 |
| halfsleeve | 35 |
| threequarter | 17 |
| thressqatar | 10 |
| sleeveless | 5 |
| sleeevless | 3 |
| capsleeves | 3 |
| cap-sleeves | 2 |
| half | 1 |
| Petal | 1 |
| urndowncollor | 1 |
| turndowncollor | 1 |
| sleveless | 1 |
| butterfly | 1 |
| threequater | 1 |
Ouch, so many misspelled words. This is when my brain is racking up all the ways I can automate this problem away. Hence my stumbling upon Markus' post.
Automagically Correcting Data
First, I decided to completely ignore what Markus warns in his post and automatically correct all the words in that column.
To begin the code, let's import and create an instance of the spellchecker:
from hunspell import HunSpell
spellchecker = HunSpell('/usr/share/hunspell/en_US.dic', '/usr/share/hunspell/en_US.aff')
I modified his correct_words function so that it only corrects one word and so I can apply it along the SleeveLength column.
def correct_word(checker, word, add_to_dict=[]):
"Takes in a hunspell object and a word and corrects the word if needed"
# Add custom words to the dictionary
for w in add_to_dict:
checker.add(w)
corrected = ""
# Check to see if it's a string
if isinstance(word, str):
# Check the spelling
ok = checker.spell(word)
if not ok:
# Grab suggestions for misspelled word
suggestions = checker.suggest(word)
if suggestions:
# Grab the best suggestion
best = suggestions[0]
corrected = best
else:
# There are no suggestions for misspelled word, return the original
corrected = word
else:
# Word is spelled correctly
corrected = word
else:
## Not a string. Return original
corrected = word
return corrected
Now let's apply the function over the SleeveLength column of the dataset:
dresses_data['SleeveLength'] = dresses_data['SleeveLength'].apply(
lambda x: correct_word(spellchecker, x))
Doing so creates the following series:
| Word | Frequency |
|---|---|
| sleeveless | 232 |
| full | 97 |
| short | 96 |
| half sleeve | 35 |
| three quarter | 17 |
| throatiness | 10 |
| cap sleeves | 3 |
| cap-sleeves | 2 |
| Petal | 1 |
| butterfly | 1 |
| turndowncollor | 1 |
| half | 1 |
| landownership | 1 |
| forequarter | 1 |
As you might be able to tell, this process didn't go as intended. landownership isn't even a length of a sleeve!
Reporting Misspelled Items and Allowing User Intervention
This is when I have to remember, technology isn't perfect. Instead we should rely on ourselves to identify what the word should be correctly spelled as.
Keeping that in mind, I modified the function again to take in a list of the data, and return a dictionary that has the misspelled words as the keys and suggestions as the values represented as a list.
def list_word_suggestions(checker, words, echo = True, add_to_dict=[]):
"Takes in a list of words and returns a dictionary with mispellt words as keys and suggestions as a list. Also prints it out"
# add custom words to the dictionary
for w in add_to_dict:
checker.add(w)
suggestions = {}
for word in words:
if isinstance(word, str):
ok = checker.spell(word)
if not ok and word not in suggestions:
suggestions[word] = checker.suggest(word)
if not suggestions[word] and echo:
print(word + ": No suggestions")
elif echo:
print(word + ": " + "[", ", ".join(repr(i) for i in suggestions[word]), "]")
return suggestions
With that, I can use the function on my data. To do so, I convert the pandas values to a list and pass it to the function:
s = list_word_suggestions(spellchecker, dresses_data['SleeveLength'].values.tolist())
These are the suggestions it produces:
sleevless: [ 'sleeveless', 'sleepless', 'sleeves', 'sleekness', 'sleeve', 'lossless' ]
threequarter: [ 'three quarter', 'three-quarter', 'forequarter' ]
halfsleeve: ['half sleeve', 'half-sleeve', 'sleeveless' ]
turndowncollor: No suggestions
threequater: [ 'forequarter' ]
capsleeves: [ 'cap sleeves', 'cap-sleeves', 'capsules' ]
sleeevless: [ 'sleeveless', 'sleepless', 'sleeves', 'sleekness', 'sleeve' ]
urndowncollor: [ 'landownership' ]
thressqatar: [ 'throatiness' ]
sleveless: [ 'sleeveless', 'levelness', 'valveless', 'loveless', 'sleepless' ]
From here, you can analyze the output and do the replacements yourself:
dresses_data['SleeveLength'].replace('sleevless', 'sleeveless', inplace = True)
What's the Benefit?
This is where you ask "What's the difference if it doesn't automatically fix my data?"
When you have large datasets, it can be hard to individually identify which items are misspelled. Using this method will allow you to have a list of all the items that are misspelled which can let you deal with it in a systematic way.