text processing applications, such as machine translation systems, information retrieval systems or natural-language understanding systems, need to identify multi-word expressions that refer to proper names of people, organizations, places, laws and other entities. when encountering mrs. candy hill in input text, for example, a machine translation system should not attempt to look up the translation of candy and hill, but should translate mrs. to the appropriate personal title in the target language and preserve the rest of the name intact. similarly, an information retrieval system should not attempt to expand candy to all of its morphological variants or suggest synonyms (wacholder et al. 1994). the need to identify proper names has two aspects: the recognition of known names and the discovery of new names. since obtaining and maintaining a name database requires significant effort, many applications need to operate in the absence of such a resource. without a database, names need to be discovered in the text and linked to entities they refer to. even where name databases exist, text needs to be scanned for new names that are formed when entities, such as countries or commercial companies, are created, or for unknown names which become important when the entities they refer to become topical. this situation is the norm for dynamic applications such as news providing services or internet information indexing. the next section describes the different types of proper name ambiguities we have observed. section 3 discusses the role of context and world knowledge in their disambiguation; section 4 describes the process of name discovery as implemented in nominator, a module for proper name recognition developed at the ibm t.j. watson research center. sections 5-7 elaborate on nominator's disambiguation heuristics.sections 5-7 elaborate on nominator's disambiguation heuristics. ambiguity remains one of the main challenges in the processing of natural language text. because of these difficulties, we believe that for the forseeable future, practical applications to discover new names in text will continue to require the sort of human effort invested in nominator. text processing applications, such as machine translation systems, information retrieval systems or natural-language understanding systems, need to identify multi-word expressions that refer to proper names of people, organizations, places, laws and other entities. an evaluation of an earlier version of nominator, was performed on 88 wall street journal documents (nist 1993) that had been set aside for testing. in the rest of the paper we describe the resources and heuristics we have designed and implemented in nominator and the extent to which they resolve these ambiguities. name identification requires resolution of a subset of the types of structural and semantic ambiguities encountered in the analysis of nouns and noun phrases (nps) in natural language processing. all of these ambiguities must be dealt with if proper names are to be identified correctly. it assigns weak types such as ?human or fails to assign a type if the available information is not sufficient.