- 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 v1. - Ensure that the output only contains the result, without explanations or additional text.
hi universe
But how to implement this without knowing specific rules for name detection? Maybe the user expects a general approach where any capitalized word is considered a name unless it's a common noun. However, this could lead to errors because some common nouns might start with a capital letter. For example, in the phrase "Apple is a fruit," "Apple" is a common noun and should have variants like "apple, orange, banana," but if it's part of a sentence where it's a company, it should stay "Apple." - Tokenize the input text into words or named entities
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. - If not, generate three variants and format as v1
Now, to apply this to the given example. Since the user provided the example of converting "hello world" to "heyuniverse", I can infer that each regular word is transformed, while names are kept. Therefore, if the input text includes names like email addresses or addresses, they remain the same, and other words get transformed. Maybe the user expects a general approach where
- 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 v1. - Ensure that the output only contains the result, without explanations or additional text.
hi universe
But how to implement this without knowing specific rules for name detection? Maybe the user expects a general approach where any capitalized word is considered a name unless it's a common noun. However, this could lead to errors because some common nouns might start with a capital letter. For example, in the phrase "Apple is a fruit," "Apple" is a common noun and should have variants like "apple, orange, banana," but if it's part of a sentence where it's a company, it should stay "Apple."
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.
Now, to apply this to the given example. Since the user provided the example of converting "hello world" to "heyuniverse", I can infer that each regular word is transformed, while names are kept. Therefore, if the input text includes names like email addresses or addresses, they remain the same, and other words get transformed.