A Named Entity Recognition System for Dutch
نویسندگان
چکیده
We describe a Named Entity Recognition system for Dutch that combines gazetteers, handcrafted rules, and machine learning on the basis of seed material. We used gazetteers and a corpus to construct training material for Ripper, a rule learner. Instead of using Ripper to train a complete system, we used many different runs of Ripper in order to derive rules which we then interpreted and implemented in our own, hand-crafted system. This speeded up the building of a hand-crafted system, and allowed us to use many different rule sets in order to improve performance. We discuss the advantages of using machine learning software as a tool in knowledge acquisition, and evaluate the resulting system for Dutch.
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