Smarr, Joseph and Manning, Christopher D. (2002) Classifying Unknown Proper Noun Phrases Without Context. Technical Report. Stanford.
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.
|Item Type:||Techreport (Technical Report)|
|Uncontrolled Keywords:||named-entity classification, unknown words, probabilistic modeling, n-grams|
|Related URLs:||Project Homepage||http://www-nlp.stanford.edu/|
|Deposited By:||Import Account|
|Deposited On:||29 Sep 2002 17:00|
|Last Modified:||25 Dec 2008 10:08|
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