How to use Python pomegranate library to implement a spelling checker based on Bayesian network

1. Prepare data

We use Peter Norvig’s “big.txt” text file as a sample data set. This data set contains a large number of words from English articles, and the upper and lower case has been unified to lower case. We need to read the file line by line and use the re library in Python to perform preliminary processing of the text:

import re
# 读取文本并进行预处理
with open('big.txt') as f:
    texts = f.readlines()
# 清洗数据，去掉数字和标点符号
words = []
for t in texts:
    words += re.findall(r'\w+', t.lower())

2. Build a Bayesian network

We need to build a Bayesian network To handle the spell checker task, the network contains 3 nodes: hidden state (correct spelling), incorrect observation, and correct observation. The implicit state is the causal node, and the wrong observation node and the correct observation node directly depend on the implicit state node.

The following is the code to establish the Bayesian network:

from pomegranate import *
# 建立隐因节点
correct_spell = State(DiscreteDistribution(dict.fromkeys(words, 1)), name='Correct_Spelling')
# 建立观察节点（错误拼写和正确拼写）
letter_dist = {}
for w in words:
    for l in w:
        if l not in letter_dist:
            letter_dist[l] = len(letter_dist)
error_spelling = State(DiscreteDistribution(letter_dist), name='Error_Spelling')
correct_spelling_observed = State(DiscreteDistribution(letter_dist), name='Correct_Spelling_Observed')
# 建立连边关系
model = BayesianNetwork('Spelling Correction')
model.add_states(correct_spell, error_spelling, correct_spelling_observed)
model.add_edge(correct_spell, error_spelling)
model.add_edge(correct_spell, correct_spelling_observed)
model.bake()

3. Training model

After the data is ready, we can start training the Bayesian network. During training, we need to estimate network parameters based on observed data.

The following is the code for training the Bayesian Network:

# 利用语料库训练贝叶斯网络
for word in words:
    model.predict(word)
# 打印结果（即每个字母在不同位置出现的统计概率）
print(error_spelling.distribution.parameters[0])

As you can see from the results generated in the above code, during the training process, BayesianNetwork learns the occurrence of different letters in words in the sample data The probability distribution of times can better capture the correct grammatical structure of English words.

4. Test model

After the training is completed, we can use the Bayesian network and use the Viterbi algorithm to find the optimal path for spelling correction.

The following is the code to test the Bayesian network:

from pomegranate import *
# 定义输入单词
test_word = 'speling'
# 将输入单词转换为列表
letters = list(test_word)
# 遍历该输入单词中的所有字母，并将每个字母的错误概率加起来（实际上就是计算“错误观察”节点的联合概率）
error_prob = sum([error_spelling.distribution.probability(l) for l in letters])
# 构建“正确观察”节点的联合概率矩阵
correct_prob = [[''.join(letters[k:j]) for j in range(k+1, len(letters)+1)] for k in range(len(letters))]
# 利用Viterbi算法查找最优路径（即最可能的正确单词）
corrected_word = max(model.viterbi(correct_prob)[1], key=lambda x: x[1])[0]
# 打印结果
print('Original word:', test_word)
print('Corrected word:', corrected_word)

In the above code, we convert the input words into a list of characters and iterate over them. The sum of error probabilities for all characters is then calculated and a joint probability matrix of "correctly observed" nodes is constructed. Finally, the Viterbi algorithm is used to find the optimal path (that is, the word with the highest probability) and output it as the result of automatic correction.

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