Paper: Unsupervised Learning Of Name Structure From Coreference Data

ACL ID N01-1007
Title Unsupervised Learning Of Name Structure From Coreference Data
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2001
Authors

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.