Identifying Unknown Proper Names in Newswire Text

Identifying Unknown Proper Names in Newswire Text Inderjeet Mani, T. Richard Macmillan, Susann Luperfoy, Elaine P. Lusher, Sharon J. Laskowski Artificial Intelligence Technical Center The MITRE Corporation, Mail Slop Z,~01 7525 Colshire Drive, McLean, Virginia 22102-3481 mani @starbase. mitre, org Abstract 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 pre- processor 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. 1 Introduction 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 pre-processor for more knowledge intensive tasks such as database extraction. With the growing use of tagged corpora in a variety of language-related research areas, being able to reliably tag proper names is an obvious advantage. In addition, the development of practical techniques for name identification help to shed light on the various uses of proper names in text. Traditional approaches to unknown proper name identification involve, broadly speak- ing, the lexical lookup of names or name fragments in a name database. For example, approaches such as [Aone et al., 92], [Aberdeen et al., 92], and [Cowie et al., 92], iden- tify person names by marking off phrases which contain unknown words close to known name elements like first or last names, and (in [Cowie et al., 92]) unknown words close to specific title-words. As the above studies show, name databases such as cross-cultural listings of common first and last names as well as existing geographical gazetteers, are helpful in name recognition. However, approaches based exclusively on unknown words and known name elements can be confused by known common nouns (or other parts of speech) which occur in proper names, even person names. More importantly, such ap- proaches require an initial name element database. Creating such databases can be a labor-intensive task. Furthermore, no matter how large the database one can manually construct, the problem still arises of identifying names which don't happen to be present 44 in any given name database. The fact that proper names form, lexically speaking, an open class whose elements grow far more rapidly than other open classes, and the fact that they often contain other open-class elements, makes the incompleteness of such databases an obvious problem. Our approach aims at …

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