title: Classifying Unknown Proper Noun Phrases Without Context
creator: Smarr, Joseph
creator: Manning, Christopher D.
subject: Miscellaneous
description: 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.
publisher: Stanford
date: 2002-04-09
type: Techreport
type: NonPeerReviewed
format: application/pdf
identifier: http://ilpubs.stanford.edu:8090/554/1/2002-46.pdf
identifier: Smarr, Joseph and Manning, Christopher D. (2002) Classifying Unknown Proper Noun Phrases Without Context. Technical Report. Stanford.
relation: http://ilpubs.stanford.edu:8090/554/