Unsupervised learning of edit parameters for matching name variants

Since named entities are often written in different ways, question answering (QA) and other language processing tasks stand to benefit from entity matching. We address the problem of finding equivalent person names in unstructured text. Our approach is a generalization of spelling correction: We compare to candidate matches by applying a set of edits to an input name. We introduce a novel unsupervised method for learning spelling edit probabilities which improves overall F-Measure on our own name-matching task by 12%. Relevance is demonstrated by application to the GALE Distillation task.