Memory-Based Named Entity Recognition
نویسنده
چکیده
We apply a memory-based learner to the CoNLL-2002 shared task: language-independent named entity recognition. We use three additional techniques for improving the base performance of the learner: cascading, feature selection and system combination. The overall system is trained with two types of features: words and substrings of words which are relevant for this particular task. It is tested on the two language pairs that were available for this shared task: Spanish and Dutch.
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