Abstract: extracting names from natural-language text

Authors: 
Yael Ravin, TJ Watson Research Center, IBM, Yorktown Heights, NY 10598 
Nina Wacholder, CRIA, Columbia University, New York, NY 10027 

We describe Nominator, a module we developed to extract proper names from natural language
text, which is currently being integrated into IBM products and services. Using fast and
robust heuristics, Nominator locates names in text, determines what type of entity they
refer to -- such as person, place or organization -- and groups together all the variant
names that refer to the same entity. For example, "President Clinton", "Mr. Clinton" and
"Bill Clinton" are grouped as referring to the same person. Each group is assigned a
"canonical name", (e.g., "Bill Clinton") to distinguish it from other groups referring to
other entities ("Clinton, New Jersey"). Nominator produces a dictionary, or database, of
names associated with a collection of documents. dentifying the occurrences of proper names
in text and the entities they refer to can be a difficult task because of the many-to-many
mapping between names and their referents. We analyze the types of ambiguity --- structural
and semantic --- that make the discovery of proper names difficult in text, and describe
the heuristics used to disambiguate names in Nominator, a fully-implemented module for
proper name recognition developed at the IBM T.J. Watson Research Center.