TypeError：序列项 1：预期的 str 实例，未找到 NoneType

Question

我创建了这个程序来自动检查文章中的单词并提供更正，但在运行它时，我在 .join 调用中不断收到上述错误。这是代码

import nltk
from nltk.corpus import wordnet
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import WordNetLemmatizer

def autocorrect_essay(essay):
    # Tokenize the essay into sentences and words
    sentences = sent_tokenize(essay)
    corrected_sentences = []
    
    for sentence in sentences:
        words = word_tokenize(sentence)
        corrected_words = []
        
        for word in words:
            # Check if the word is misspelled
            if not wordnet.synsets(word):
                corrected_word = correct_word(word)
                corrected_words.append(corrected_word)
            else:
                corrected_words.append(word)
        
        # Join the corrected words to form a sentence
        corrected_sentence = ' '.join(corrected_words)
        corrected_sentences.append(corrected_sentence)
    
    # Join the corrected sentences to form the final essay
    corrected_essay = ' '.join(corrected_sentences)
    
    return corrected_essay

def correct_word(word):
    # Lemmatize the word to get its base form
    lemmatizer = WordNetLemmatizer()
    lemma = lemmatizer.lemmatize(word)

    # Find similar words based on Levenshtein distance
    similar_words = []
    
    for synset in wordnet.synsets(lemma):
        for lemma_name in synset.lemma_names():
            similar_words.append(lemma_name)

    # Find the most similar word based on Levenshtein distance
    min_distance = float('inf')
    best_word = None
    
    for similar_word in similar_words:
        distance = nltk.edit_distance(word, similar_word)
        
        if distance < min_distance:
            min_distance = distance
            best_word = similar_word
    
    return best_word

# Example usage
essay = "I havv a bigg problm with speling. Plese help me correct my essay."
corrected_essay = autocorrect_essay(essay)
print(corrected_essay)

我尝试过的一些事情是删除返回空的块的部分，但没有任何改变。

Answer 1

你的函数

correct_word

可以返回None。当这种情况发生时，你最终会在你的列表中添加“None”，这会破坏事情的进展。

如果根本找不到任何合适的匹配项，您可以做的一件事就是返回原始单词。

这样的东西有效（我将

best_word = None

更改为

best_word = word

）

def correct_word(word):
    # Lemmatize the word to get its base form
    lemmatizer = WordNetLemmatizer()
    lemma = lemmatizer.lemmatize(word)

    # Find similar words based on Levenshtein distance
    similar_words = set()
    
    for synset in wordnet.synsets(lemma):
        for lemma_name in synset.lemma_names():
            similar_words.add(lemma_name)

    # Find the most similar word based on Levenshtein distance
    min_distance = float('inf')
    best_word = word  # changed this line to make best_word start off as word instead of None
    
    for similar_word in similar_words:
        distance = nltk.edit_distance(word, similar_word)
        
        if distance < min_distance:
            min_distance = distance
            best_word = similar_word
    
    return best_word