Identifying Unknown Proper Names In Newswire Text

The identification of unknown proper names in text is a significant challenge for NLP systems operating on unrestricted text. A system which indexes documents according to name references can be useful for information retrieval or as a preprocessor for more knowledge intensive tasks such as database extraction. This paper describes a system which uses text skimming techniques for deriving proper names and their semantic attributes automatically from newswire text, without relying on any listing of name elements. In order to identify new names, the system treats proper names as (potentially) context-dependent linguistic expressions. In addition to using information in the local context, the system exploits a computational model of discourse which identifies individuals based on the way they are described in the text, instead of relying on their description in a pre-existing knowledge base.