Build a Simple Bayesian Spell Checker in 21 Lines of Python

Introduction

When you type a misspelled word into Google or Baidu, the engine instantly suggests the correct spelling. This article shows how to implement a similar spell‑checker in Python with only 21 lines of code.

Code

import re, collections

def words(text): return re.findall('[a-z]+', text.lower())

def train(features):
    model = collections.defaultdict(lambda: 1)
    for f in features:
        model[f] += 1
    return model

NWORDS = train(words(open('big.txt').read()))
alphabet = 'abcdefghijklmnopqrstuvwxyz'

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)

def known_edits2(word):
    return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS)

def known(words): return set(w for w in words if w in NWORDS)

def correct(word):
    candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word]
    return max(candidates, key=NWORDS.get)

Underlying Theory

The algorithm is based on a Bayesian model: we choose the candidate c that maximizes P(c|w) ∝ P(w|c)·P(c). P(c) is estimated from word frequencies in a large corpus (big.txt). P(w|c) is approximated by the edit distance between c and the misspelled word w; edits of distance 1 are far more likely than distance 2, and correct words (distance 0) are most probable.

Code Analysis

The words function extracts lowercase alphabetic tokens from the corpus. train builds a frequency dictionary, defaulting unseen words to a count of 1 using collections.defaultdict. edits1 generates all strings one edit away (deletions, transpositions, replacements, insertions). known_edits2 extends this to two‑edit candidates that exist in the dictionary. Finally, correct selects the candidate with the highest probability according to the Bayesian model.