A Survey on Unstructured Document Annotation Using Content and Query Value Based

A large data is generated in different organization which is in textual format. This contain eloquent amount of structured information, which dwell underlying in the unstructured text. Document annotation is the task of adding metadata information in the document which is useful for information extraction. Many information mining algorithms facilitate the extraction of structured information from raw data but which is costly, inefficient and also shows contaminated results. We present, an adaptive technique that Collaborative Adaptive Data sharing platform (CADS) for document annotation and use of query workload to direct the annotation process. This paper proposes survey on facilitates the generat ion of structured metadata by identifying documents containing information of interest. Such information is further useful for querying the database. Our experimental assessment shows superior results compared to approaches that rely only on the textual content or only on the query workload, to recognize attributes of importance. In this paper, we are doing a survey on document annotation techniques.