Alternatively, maybe the user is using "proper noun" in a way that's easy to identify, like any word that is capitalized, but in Vietnamese, that's not typical. So perhaps the user considers any word that's not a common noun as a proper noun. But since I can't know that unless provided a list, maybe I should proceed by assuming that all words except those that are common synonyms are proper nouns. Wait, but the user says "Don't touch proper nouns", so perhaps in the given text, the proper nouns are clear. For example, if the input is "Apple is a company", then "Apple" is a proper noun. But in Vietnamese, it could be "Táo" which is a common word, or "Apple" written in English.
- For each word in the input text: - Identify if it's a proper noun (assuming it's not in the absence of clear indicators like capitalization). - If not, find three appropriate Vietnamese synonyms. - Format the synonyms as v1. - Replace the word with this format. - Return the modified text with all applicable replacements, ensuring not to alter proper nouns.
Given these challenges, the assistant should proceed by attempting to find valid synonyms where possible, noting that some replacements might be incorrect, and handling proper nouns by leaving them as is, perhaps making an educated guess based on context. Alternatively, maybe the user is using "proper noun"
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 Tớ sùng bái đọc tập.
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. Wait, but the user says "Don't touch proper
Wait, the user's message here is the instruction, and the assistant is supposed to generate a response when given a text. So in the current scenario, the user has provided the instruction, and the assistant is to process the input text when it's given. However, in this case, the user hasn't included a "Text:" section after "Text:" in the query. The user wrote "Text:" and then "only." So perhaps the user is indicating that after providing the instruction with the example, they will now provide the actual text.
2. For each word, check if it's a proper noun. Since Vietnamese doesn't use capitalization, this might be hard. Proper nouns can be names (e.g., "Anh", "Minh"), locations ("Hà Nội", "TP.HCM"), companies ("Vingroup"), etc. Maybe the user expects me to leave any word that is a known proper noun untouched. But how to detect that? Without prior knowledge, it's tricky. Maybe the user will include proper nouns in the text, and I just need to leave them as is. So perhaps the instructions are to replace all words unless they are proper nouns, but how to determine that. - For each word in the input text:
1. Tokenize the input text into individual words. Vietnamese doesn't have spaces for words like English, so word segmentation is tricky. Wait, but the user provided the input text as a pre-tokenized version, maybe? The example input is "từ đồng nghĩa", which are three words. But in reality, Vietnamese words are separated by spaces. So assuming the input text is properly tokenized with spaces, I can split by spaces.