如何用Python字典查找同一句子中重复出现的关联词汇？

Hey there! Great job getting the sentences split into word lists already — that's a solid first step. Let's walk through how to use dictionaries to track repeated words per sentence, exactly what you need for your "associated vocab" requirement.

The Core Idea #

For each sentence's word list, we'll use a dictionary to count how many times each word appears. Any word with a count greater than 1 is your associated vocabulary for that sentence.

Step-by-Step Implementation #

Here's how to build this onto your existing code:

First, let's start with your existing split sentences structure (I'll add a sample sentences list for context, but you can replace it with your actual data):

# Your existing code (with sample sentences for demo)
sentences = [
    "I love Python, Python is fun and I love coding",
    "Hello world, world says hello to everyone",
    "Single words here have no repeats"
]

split_sentences = []
for sentence in sentences:
    # Note: If you want to clean punctuation (like commas), you might add .strip(',.!?') to each word later
    split_sentences.append(sentence.split())

Now, let's add the dictionary-based counting logic:

# Store results: a list where each entry is a dict of {word: count} for repeated words in the sentence
associated_vocab_per_sentence = []

for word_list in split_sentences:
    word_counts = {}
    # Count each word in the current sentence
    for word in word_list:
        # Optional: Use word.lower() if you want case-insensitive counting (e.g., "Python" and "python" count as same)
        # cleaned_word = word.lower().strip(',.!?')
        cleaned_word = word.strip(',.!?')  # Basic punctuation cleanup
        if cleaned_word in word_counts:
            word_counts[cleaned_word] += 1
        else:
            word_counts[cleaned_word] = 1
    
    # Filter to keep only words that appear more than once
    repeated_words = {word: count for word, count in word_counts.items() if count > 1}
    associated_vocab_per_sentence.append(repeated_words)

# Let's print the results to see how it works
for idx, vocab in enumerate(associated_vocab_per_sentence):
    print(f"Sentence {idx+1} Associated Vocab: {vocab}")

What This Does #

• Word Counting: The inner loop goes through each word in the sentence, updating the dictionary to track how many times each word shows up. We added a quick strip(',.!?') to clean up basic punctuation — you can adjust this if your text has other symbols.
• Filter Repeats: Using a dictionary comprehension, we filter out any words that only appear once, leaving just your associated vocabulary.
• Results Storage: We collect these results in a list so you can easily access the repeated words for each original sentence.

Sample Output #

Running the code above would give you:

Sentence 1 Associated Vocab: {'I': 2, 'love': 2, 'Python': 2}
Sentence 2 Associated Vocab: {'world': 2}  # If using .lower(), this would include {'hello':2, 'world':2}
Sentence 3 Associated Vocab: {}

If you want case-insensitive counting, just swap the cleaned_word line to use word.lower().strip(',.!?') — that would make "Hello" and "hello" count as the same word in sentence 2.

内容的提问来源于stack exchange，提问作者fskoft