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But since the user's exact input isn't given, the assistant might need to prompt for the text, but the user's instruction says "Output text only," so perhaps they expect a processed example output. However, without the input text, creating a sample output is the way to go.
Alright, let's tackle this query. The user wants to switch every word with three variants using the format word3, but they want to skip proper nouns. The output should be text only.
1. Tokenize the input text into words and phrases. 2. For each token, check if it's a proper noun. 3. If not, find three synonyms. 4. Replace the word with synonym1. 5. Keep proper nouns as they are.
Now, the user might not have access to a thesaurus API or a comprehensive synonym database. They might be expecting me to come up with synonyms on the fly for example sentences. But since the user hasn't provided specific text, perhaps they want a general explanation or example of how this would work.
If the user is looking for a script or a tool, they might need a programmatic solution using NLP libraries. If they just want an example, then providing an example like the one above with placeholders makes sense.
So my first step is to identify which words are proper nouns. Proper nouns are usually capitalized and refer to specific names. For example, "John", "Paris", or "Tesla". But since the user didn't provide a specific text, I need to make sure my approach works regardless of the input.
Also, the format is word2. Need to ensure that the output uses these delimiters properly. Maybe using a library like NLTK for Python could help with POS tagging and synonym retrieval, but again, the user wants the output text only.
- Tokenizing input text. - Detecting proper nouns. - Replacing other words with three synonyms. - Formatting the output correctly.
But since the user's exact input isn't given, the assistant might need to prompt for the text, but the user's instruction says "Output text only," so perhaps they expect a processed example output. However, without the input text, creating a sample output is the way to go.
Alright, let's tackle this query. The user wants to switch every word with three variants using the format word3, but they want to skip proper nouns. The output should be text only.
1. Tokenize the input text into words and phrases. 2. For each token, check if it's a proper noun. 3. If not, find three synonyms. 4. Replace the word with synonym1. 5. Keep proper nouns as they are.
Now, the user might not have access to a thesaurus API or a comprehensive synonym database. They might be expecting me to come up with synonyms on the fly for example sentences. But since the user hasn't provided specific text, perhaps they want a general explanation or example of how this would work.
If the user is looking for a script or a tool, they might need a programmatic solution using NLP libraries. If they just want an example, then providing an example like the one above with placeholders makes sense.
So my first step is to identify which words are proper nouns. Proper nouns are usually capitalized and refer to specific names. For example, "John", "Paris", or "Tesla". But since the user didn't provide a specific text, I need to make sure my approach works regardless of the input.
Also, the format is word2. Need to ensure that the output uses these delimiters properly. Maybe using a library like NLTK for Python could help with POS tagging and synonym retrieval, but again, the user wants the output text only.
- Tokenizing input text. - Detecting proper nouns. - Replacing other words with three synonyms. - Formatting the output correctly.
