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Application domains such as scientific networks, blogs, and groups share information in a huge amount; this information is usually unstructured data in text documents. Document annotation is one of the popular methods, where metadata present in document is used to search documents from a large text documents database. Attribute-value pair annotations use either content or querying value of attributes or both for annotation. Using content and querying value together increases the visibility of documents more efficiently than using them individually. Selection of attributes for annotation is called as “Attribute Suggestion Problem”. Various methods like information extraction and query forms have been used for this purpose, but they are expensive and inaccurate. We present an alternative approach, which will provide relevant attributes for annotation, having high content and querying value, with speed and accuracy.