I should also check the original text for any proper nouns, like names like John or companies like Apple, and leave them unchanged. That requires some NLP processing or at least a good understanding of English grammar to distinguish between proper and common nouns.
But if the original text includes a proper noun like "She visited Paris last year," then "Paris" should remain unchanged, so the alteration would be "She visited Paris last year." Here, "visited" is changed to three synonyms, but "Paris" stays the same. -18 - Sex-Positive.2024.720p.WEBRip.-CM-.mp4
Another thing to consider is the structure of the sentences. Changing words to their options shouldn't result in grammatically incorrect sentences. For example, changing "fox" to dog should still make sense in the context. But if the sentence is "The fox is red," changing "fox" to "dog" would still work, but "bird" might require adjusting the sentence structure or other words to fit better. I should also check the original text for
I also need to handle edge cases where a word might have multiple meanings. For example, "bank" could refer to a financial institution or the side of a river. In such cases, I need to provide options that fit the context. If the original sentence is "We sat by the bank of the river," changing "bank" to shore makes sense, whereas changing "bank" in "I deposited money in the bank" would require options like financial institution. Another thing to consider is the structure of the sentences
In summary, the process involves: 1. Identifying all replaceable words (excluding proper nouns). 2. Generating three appropriate options for each. 3. Formatting each word as opt2. 4. Ensuring grammatical correctness and context fit. 5. Leaving proper nouns untouched.
I need to make sure that the replacement words fit the context appropriately. For instance, if the original word is in the past tense, the replacements should also be past tense unless it's part of changing the word entirely. Similarly, adjectives and adverbs might need different forms based on the context.
Another consideration is the length of the text. If the user's input is a long paragraph, processing each word efficiently without missing any is crucial. But since the user hasn't provided specific text, I might need to prompt them for the actual content to work on.