Spell Checker for Python

Question

I'm fairly new with Python and NLTK. I am busy with an application that can perform spell checks(replaces the incorrectly spelled word with the correctly spelled word), Im currently using the Enchant Library on Python-2.7, PyEnchant and the NLTK library. The code below is the class that handles the correction/replacement.

from nltk.metrics import edit_distance

class SpellingReplacer(object):
    def __init__(self, dict_name = 'en_GB', max_dist = 2):
        self.spell_dict = enchant.Dict(dict_name)
        self.max_dist = 2

    def replace(self, word):
        if self.spell_dict.check(word):
            return word
        suggestions = self.spell_dict.suggest(word)

        if suggestions and edit_distance(word, suggestions[0]) <= self.max_dist:
            return suggestions[0]
        else:
            return word

I have written a function that takes in a list of words and performs the def replace on each word and return a list of the words but spelled correctly.

def spell_check(word_list):
    checked_list = []
    for item in word_list:
        replacer = SpellingReplacer()
        r = replacer.replace(item)
        checked_list.append(r)
    return checked_list

>>> word_list = ['car', 'colour']
>>> spell_check(words)
['car', 'color']

Now I don't really like this because it isn't very accurate and I'm looking for a way to achieve spelling checks and replacements on words. I also need something that can pick up spelling mistakes like "caaaar"? Are there better ways to perform spelling checks out there? If so what are they? How does Google do it for example because their spelling suggester is very good? Any suggestions

Answer 1

I'd recommend starting by carefully reading this post by Peter Norvig. (I had to something similar and I found it extremely useful.)

The following function, in particular has the ideas that you now need to make your spell checker more sophisticated: splitting, deleting, transposing, and inserting the irregular words to 'correct' them.

def edits1(word):
   splits     = [(word[:i], word[i:]) for i in range(len(word) + 1)]
   deletes    = [a + b[1:] for a, b in splits if b]
   transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1]
   replaces   = [a + c + b[1:] for a, b in splits for c in alphabet if b]
   inserts    = [a + c + b     for a, b in splits for c in alphabet]
   return set(deletes + transposes + replaces + inserts)

Note: The above is one snippet from Norvig's spelling corrector

And the good news is that you can incrementally add to and keep improving your spell-checker.

Hope that helps.

Answer 2

You can use the autocorrect lib to spell check in python.
Example Usage:

from autocorrect import spell

print spell('caaaar')
print spell(u'mussage')
print spell(u'survice')
print spell(u'hte')

Result:

caesar
message
service
the

Answer 3

The best way for spell checking in python is by: SymSpell, Bk-Tree or Peter Novig's method.

The fastest one is SymSpell.

This is Method1: Reference link pyspellchecker

This library is based on Peter Norvig's implementation.

pip install pyspellchecker

from spellchecker import SpellChecker

spell = SpellChecker()

# find those words that may be misspelled
misspelled = spell.unknown(['something', 'is', 'hapenning', 'here'])

for word in misspelled:
    # Get the one `most likely` answer
    print(spell.correction(word))

    # Get a list of `likely` options
    print(spell.candidates(word))

Method2: SymSpell Python

pip install -U symspellpy

Answer 4

spell corrector->

you need to import a corpus on to your desktop if you store elsewhere change the path in the code i have added a few graphics as well using tkinter and this is only to tackle non word errors!!

def min_edit_dist(word1,word2):
    len_1=len(word1)
    len_2=len(word2)
    x = [[0]*(len_2+1) for _ in range(len_1+1)]#the matrix whose last element ->edit distance
    for i in range(0,len_1+1):  
        #initialization of base case values
        x[i][0]=i
        for j in range(0,len_2+1):
            x[0][j]=j
    for i in range (1,len_1+1):
        for j in range(1,len_2+1):
            if word1[i-1]==word2[j-1]:
                x[i][j] = x[i-1][j-1]
            else :
                x[i][j]= min(x[i][j-1],x[i-1][j],x[i-1][j-1])+1
    return x[i][j]
from Tkinter import *


def retrieve_text():
    global word1
    word1=(app_entry.get())
    path="C:\Documents and Settings\Owner\Desktop\Dictionary.txt"
    ffile=open(path,'r')
    lines=ffile.readlines()
    distance_list=[]
    print "Suggestions coming right up count till 10"
    for i in range(0,58109):
        dist=min_edit_dist(word1,lines[i])
        distance_list.append(dist)
    for j in range(0,58109):
        if distance_list[j]<=2:
            print lines[j]
            print" "   
    ffile.close()
if __name__ == "__main__":
    app_win = Tk()
    app_win.title("spell")
    app_label = Label(app_win, text="Enter the incorrect word")
    app_label.pack()
    app_entry = Entry(app_win)
    app_entry.pack()
    app_button = Button(app_win, text="Get Suggestions", command=retrieve_text)
    app_button.pack()
    # Initialize GUI loop
    app_win.mainloop()

Answer 5

from autocorrect import spell for this u need to install, prefer anaconda and it only works for words, not sentences so that's a limitation u gonna face.

from autocorrect import spell print(spell('intrerpreter')) output: interpreter

Answer 6

Maybe it is too late, but I am answering for future searches. TO perform spelling mistake correction, you first need to make sure the word is not absurd or from slang like, caaaar, amazzzing etc. with repeated alphabets. So, we first need to get rid of these alphabets. As we know in English language words usually have a maximum of 2 repeated alphabets, e.g., hello., so we remove the extra repetitions from the words first and then check them for spelling. For removing the extra alphabets, you can use Regular Expression module in Python.

Once this is done use Pyspellchecker library from Python for correcting spellings.

For implementation visit this link: https://rustyonrampage.github.io/text-mining/2017/11/28/spelling-correction-with-python-and-nltk.html