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In order to build accurate target profiles, most definition question answering (QA) systems primarily involve utilizing various external resources, such as WordNet, Wikipedia, Biograpy.com, etc. However, these external resources are not always available or helpful when answering definition questions. In contrast, this paper proposes an unsupervised classification model, called the U-Model, which can liberate definitional QA systems from heavily depending on a variety of external resources via applying sentence expansion ($SE$) and SVM classifier. Experimental results from testing on English TREC test sets reveal that the proposed U-Model can not only significantly outperform baseline system but also require no specific external resources.