Recent rapid increase in the generation of clinical data and rapid development of computational science make us able to extract new insights from massive datasets in healthcare industry. Oncological Electronic Health Records (EHRs) are creating rich databases for documenting patient’s history and they potentially contain a lot of patterns that can help in better management of the disease. However, these patterns are locked within free text (unstructured) portions of EHRs and consequence in limiting health professionals to extract useful information from them and to finally perform Query and Answering (Q&A) process in an accurate way. The Information Extraction (IE) process requires Natural Language Processing (NLP) techniques to assign semantics to these patterns. Therefore, in this paper, we analyze the design of annotators for specific lung cancer concepts that can be integrated over Apache Unstructured Information Management Architecture (UIMA) framework. In addition, we explain the details of generation and storage of annotation outcomes.