3. The method of claim 1 , wherein the name model includes: a collection of the n-grams from the annotated semi-structured data identifying names and the large annotated corpus identifying names, wherein each n-gram includes a family name as a left character and a given name as right context, and each n-gram has a corresponding probability of identifying a respective name.
1. A method implemented by data processing apparatus, the method comprising: generating a raw name detection model using a collection of family names and an annotated corpus including a collection of n-grams, each n-gram having a corresponding probability of occurring as a respective name in the annotated corpus; applying the raw name detection model to a collection of semi-structured data to form annotated semi-structured data, the annotated semi-structured data identifying n-grams identifying names and n-grams not identifying names; applying the raw name detection model to a large unannotated corpus to form a large annotated corpus data identifying n-grams of the large unannotated corpus identifying names and n-grams not identifying names; and generating a name detection model including: deriving a name model using the annotated semi-structured data identifying names and the large annotated corpus data identifying names, deriving a not-name model using the semi-structured data not identifying names, deriving a language model using the large annotated corpus; and wherein generating and applying are performed by data processing apparatus.