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Abstract : |
It is often claimed that Named Entity recognition systems need extensive gazetteers|lists of names of people, organisations, locations, and other named entities. Indeed, the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named Entity recognition systems. We report on a Named Entity recognition system which combines rule-based grammars with statistical (maximum entropy) models. We report on the system 's performance with gazetteers of different types and dierent sizes, using test material from the muc{7 competition. We show that, for the text type and task of this competition, it is sucient to use relatively small gazetteers of well-known names, rather than large gazetteers of low-frequency names. We conclude with observations about the domain independence of the competition and of our experiments., |
