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Nowadays, most of information saved in companies are as unstructured models. Retrieval and extraction of the information is essential works and importance in semantic web areas. Many of these requirements will be depend on the storage efficiency and unstructured data analysis. Merrill Lynch recently estimated that more than 80% of all potentially useful business information is unstructured data. The large number and complexity of unstructured data opens up many new possibilities for the analyst. We analyze both structured and unstructured data individually and collectively. Text mining and natural language processing are two techniques with their methods for knowledge discovery form textual context in documents. In this study, text mining and natural language techniques will be illustrated. The aim of this work comparison and evaluation the similarities and differences between text mining and natural language processing for extraction useful information via suitable themselves methods.