Named Entity Recognition as a House of Cards: Classifier Stacking
JOHNS HOPKINS UNIV BALTIMORE MD CENTER FOR LANGUAGE AND SPEECH PROCESSING (CLSP)
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This paper presents a classifier stacking-based approach to the named entity recognition task NER henceforth. Transformation-based learning Brill, 1995, Snow sparse network of winnows Mu oz et al., 1999 and a forward-backward algorithm are stacked the output of one classifier is passed as input to the next classifier, yielding considerable improvement in performance. In addition, in agreement with other studies on the same problem, the enhancement of the feature space in the form of capitalization information is shown to be especially beneficial to this task.
- Numerical Mathematics