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4 CONCLUSIONS AND FUTURE
WORK
This paper presents a text mining method for extract-
ing collocation candidates and identifying Named En-
tities from a list of candidates. The filtering method
uses only a statistical approach based on the Dice
measure and exploitation of the results of search en-
gines. The NE is ”stable”, which is why we built vari-
ations of the candidates and checked their popularity
using search engines. If the candidates we built turned
out to be irrelevant (i.e. low value of the statistical
measure), they were considered as NE.
In the next step of our work, we plan to enrich the
rules concerning variations, which is a precondition
to take into account the vast majority of possible lin-
guistic variations. Finally, we plan to combine this
approach, which is only based on statistical knowl-
edge, with lexical information, particularly the use of
uppercase letters when possible.
ACKNOWLEDGEMENTS
I thank Daphne Goodfellow who improved the read-
ability of this paper.
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HOW STATISTICAL INFORMATION FROM THE WEB CAN HELP IDENTIFY NAMED ENTITIES
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