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In conclusion, my approach is:

Testing this process with a sample input might help. Let's take a sentence: "John changed the text." The word "John" is a name, so it stays. "Changed" becomes adjusted, "the" becomes this, "text" becomes document. The result would be: John changed that article. fifty shades of grey download telegram link

1. Tokenize the input text into words, names, and other elements. 2. For each word that is not a name, find three synonyms. 3. Replace the word with syn1. 4. Ensure names remain unchanged. 5. Output the transformed text with only the result. In conclusion, my approach is: Testing this process

Another consideration: handling words with multiple parts of speech. For example, "run" can be a verb or a noun. The synonyms might differ based on part of speech. Since I don't have part-of-speech tagging in this scenario, I'll have to make an educated guess. But again, the user might not care as long as the synonyms are plausible. The result would be: John changed that article