We present two methods for learning the struc- ture of personal names from unlabeled data. The rst simply uses a few implicit constraints governing this structure to gain a toehold on the problem | e.g., descriptors come before rst names, which come before middle names, etc. The second model also uses possible coreference information. We found that coreference con- straints on names improve the performance of the model from 92.6% to 97.0%. We are in- terested in this problem in its own right, but also as a possible way to improve named entity recognition (by recognizing the structure of dif- ferent kinds of names) and as a way to improve noun-phrase coreference determination.

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