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Factoid Question Answering is an exciting area of Natural Language Engineering that has the potential to replace one major use of search engines today. In this disserta-tion, I introduce a new method of handling factoid questions whose answers are proper names. The method, Topic Indexing and Retrieval, addresses two issues that prevent current factoid QA system from realising this potential: They can’t satisfy users ’ de-mand for almost immediate answers, and they can’t produce answers based on evi-dence distributed across a corpus. The first issue arises because the architecture common to QA systems is not easily scaled to heavy use because so much of the work is done on-line: Text retrieved by information retrieval (IR) undergoes expensive and time-consuming answer extraction while the user awaits an answer. If QA systems are to become as heavily used as popular web search engines, this massive process bottle-neck must be overcome. The second issue of how to make use of the distributed evidence in a corpus is rel-evant when no single passage in the corpus provides sufficient evidence for an answe