- Tokenize the input text into words or named entities. - For each token: - Check if it's a name (email, address, URL, proper noun). - If yes, leave it unchanged. - If not, generate three variants and format as v2. - Ensure that the output only contains the result, without explanations or additional text.
I need to make sure that in the output, only the result is presented, without any additional text or explanation. The user wants the final converted text directly. - Tokenize the input text into words or named entities
Given the ambiguity, perhaps the user expects us to treat any sequence that looks like an email, URL, or address as a name and leave them as-is, while generating variants for other words. So, the main task is to split the text into tokens that are either names or words. - If not, generate three variants and format as v2
Alright, let's tackle this request. The user wants me to convert every word into three variants, formatted as v1, while keeping names intact and only providing the result. Hmm, first I need to make sure I understand correctly. They mentioned "every word," but they want to leave names (like proper nouns) as they are. So, if a word is a name, I shouldn't replace it with variants. Otherwise, each regular word should have three possible substitutes separated by vertical bars. The user wants the final converted text directly
Looking back at the example, "example@example.com" would be considered a name, so it remains unchanged. "123 Main St, Anytown, USA 12345" is an address, so that's a name. Then the rest of the words, if any, would be converted. However, in the provided example, there's no other text. The user included "example@example.com" and "123 Main St, Anytown, USA 12345" as placeholders.
Assuming the actual text to process is "example@example.com 123 Main St, Anytown, USA 12345", the output would keep those as is. But the user might have intended that even in their absence, the example shows the process.
Another consideration is generating appropriate variants. For each word, we need three plausible synonyms or variations. For example, converting "Hello" to "Hi, Greetings, Hey". However, the user might expect different variants depending on context. But without knowing the actual text, it's hard to tailor the variants precisely.