Unsupervised Learning of Name Structure From Coreference Data

Unsupervised Learning of Name Structure From Coreference Data∗ Eugene Charniak Brown Laboratory for Linguistic Information Processing Department of Computer Science Brown University, Box 1910, Providence, RI ec@cs.brown.edu Abstract We present two methods for learning the struc- ture of personal names from unlabeled data. The first simply uses a few implicit constraints governing this structure to gain a toehold on the problem — e.g., descriptors come before first 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. 1 Introduction We present two methods for the unsupervised learning of the structure of personal names as found in Wall Street Journal text. More specif- ically, we consider a “name” to be a sequence of proper nouns from a single noun-phrase (as in- dicated by Penn treebank-style parse trees). For example, “Defense Secretary George W. Smith” would be a name and we would analyze it into the components “Defense Secretary” (a descrip- tor), “George” (a first name), “W.” (a middle name, we do not distinguish between initials and “true” names), and “Smith” (a last name). We consider two unsupervised models for learning this information. The first simply uses a few implicit constraints governing this struc- ture to gain a toehold on the problem — e.g., descriptors come before first names, which come ∗ This research was supported in part by NSF grant LIS SBR 9720368. The author would like to thank Mark Johnson and the rest of the Brown Laboratory for Lin- guistic Information Processing (BLLIP) for general ad- vice and encouragement. before middle names, etc. We henceforth call this the “name” model. The second model also uses possible coreference information. Typically the same individual is mentioned several times in the same article (e.g., we might later en- counter “Mr. Smith”), and the pattern of such references, and the mutual constraints among them, could very well help our unsupervised methods determine the correct structure. We call this the “coreference” model. We were at- tracted to this second model as it might offer a small example of how semantic information like coreference could help in learning structural in- formation. To the best of our knowledge there has not been any previous work on learning personal structure. We are aware of one previous case of unsupervised learning of lexical information from possible coreference, namely that of Ge et. al. [5] where possible pronoun coreference was used to learn the gender of nouns. In this case a program with an approximately 65% accuracy in determining the correct antecedent was used to collect information on pronouns and their possible antecedents. The gender of the pro- noun was then used to suggest the gender …

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