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In this paper, we describe a discriminative learning approach for question answering. Our training corpus consists of (2) million Frequently Asked Questions (FAQs) and their corresponding answers that we mined from the World Wide Web. This corpus is used to train the lexical and semantic association model between questions and answers. We evaluate our approach on two question answering tasks: 2003 Text Retrieval Conference Question Answering task, and finding answers to FAQs. In both cases, the proposed approach achieved significant improvements over the results for an information retrieval based question answering model.