%0 Report
%9 Technical Report
%A Smarr, Joseph
%A Manning, Christopher D.
%D 2002
%F ilprints:554
%I Stanford InfoLab
%K named-entity classification, unknown words, probabilistic modeling, n-grams
%T Classifying Unknown Proper Noun Phrases Without Context
%U http://ilpubs.stanford.edu:8090/554/
%X We present a probabilistic generative model used to classify unknown Proper Noun Phrases into semantic categories.  The core of the classifier is an n-gram character model, which is enhanced with an n-gram word-length model and a common word model.  While most work has depended largely on context or domain-specific rules for semantic disambiguation of unknown names, we demonstrate that there is surprisingly reliable statistical information available in the composition of the names themselves.  Using the context-independent probabilities assigned by our domain independent classifier is sufficient to achieve greater than 90% classification accuracy on typical tasks.