But wait, the instruction says "modify all words with 3 synonyms". So maybe each word is turned into a list of three synonyms, separated by | within curly brackets. So the whole text is transformed into a version where each word is replaced by three synonyms, allowing the user to choose one for each. For example, if the original text was "Tôi thích đọc sách", the modified text would be Tôi thích xem sách.
The challenge is in steps 3a and 3b. For step 3a, identifying proper nouns in Vietnamese is non-trivial without linguistic tools. The assistant might need to rely on external resources or heuristics. But wait, the instruction says "modify all words
Now, the challenge is identifying proper nouns in Vietnamese. Since Vietnamese doesn't capitalize words, proper nouns might be context-based. For example, names of people, locations, brands. If the text mentions "Hà Nội", that's a proper noun. But how to distinguish it from other words? Maybe the user expects me to leave any word that is likely a proper noun as is. So perhaps the user is using proper nouns in the text, and I need to identify and not replace them. For example, if the original text was "Tôi
1. Receive the text input from the user. 2. Tokenize the input into words. 3. For each word: a. Check if it's a proper noun. If yes, leave it as is. b. If not, find three synonyms. 4. Replace each non-proper noun word with syn3. 5. Output the modified text. The assistant might need to rely on external
In that case, the assistant needs to outline the steps: