Named Entity Recognition without Gazetteers

Proceedings of EACL '99 Named Entity Recognition without Gazetteers Andrei Mikheev, Marc Moens and Claire Grover HCRC Language Technology Group, University of Edinburgh, 2 Buccleuch Place, Edinburgh EH8 9LW, UK. mikheev@harlequin, co. uk M. Moens@ed. ac. Uk C. Grover@ed. ac. uk Abstract It is often claimed that Named En- tity recognition systems need extensive gazetteers--lists of names of people, or- ganisations, locations, and other named entities. Indeed, the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named En- tity recognition systems. We report on a Named Entity recogni- tion system which combines rule-based grammars with statistical (maximum en- tropy) models. We report on the sys- tem's performance with gazetteers of dif- ferent types and different sizes, using test material from the MUC-7 competition. We show that, for the text type and task of this competition, it is sufficient to use relatively small gazetteers of well-known names, rather than large gazetteers of low-frequency names. We conclude with observations about the domain indepen- dence of the competition and of our ex- periments. 1 Introduction Named Entity recognition involves processing a text and identifying certain occurrences of words or expressions as belonging to particular cate- gories of Named Entities (NE). NE recognition software serves as an important preprocessing tool for tasks such as information extraction, informa- tion retrieval and other text processing applica- tions. What counts as a Named Entity depends on the application that makes use of the annotations. One such application is document retrieval or au- tomated document forwarding: documents an- noted with NE information can be searched more "Now also at Harlequin Ltd. (Edinburgh office) accurately than raw text. For example, NE an- notation allows you to search for all texts that mention the company "Philip Morris", ignoring documents about a possibly unrelated person by the same name. Or you can have all documents forwarded to you about a person called "Gates", without receiving documents about things called gates. In a document collection annotated with Named Entity information you can more easily find documents about Java the programming lan- guage without getting documents about Java the country or Java the coffee. Most common among marked categories are names of people, organisations and locations as well as temporal and numeric expression. Here is an example of a text marked up with Named Entity information: <ENAMEX TYPE='PERSON' >Flavel Donne</ENAMEX> is an analyst with <ENAMEX TYPE= ' ORGANIZATION ' >General Trends </ENAMEX>, which has been based in <ENAMEX TYPE='LOCATION'>Little Spring</ENAMEX> since <TIMEX TYPE='DATE' >July 1998</TIMEX>. In an article on the Named Entity recognition competition (part of MUC-6) Sundheim (1995) re- marks that "common organization names, first names of people and location names can be han- dled by recourse to list lookup, although there are drawbacks" (Sundheim 1995: 16). In fact, par- ticipants in that competition from the Univer- sity of Durham (Morgan et al., 1995) and from SRA (Krupka, 1995) report that gazetteers did not make that much of a difference to their sys- tem. Nevertheless, in …

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