Evaluating Corpora for Named Entity Recognition Using Character-Level Features

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Abstract

We present a new collection of training corpora for evaluation of language-independent named entity recognition systems. For the five languages included in this initial release, Basque, Dutch, English, Korean, and Spanish, we provide an analysis of the relative difficulty of the NER task for both the language in general, and as a supervised task using these corpora. We construct three strongly language-independent systems, each using only orthographic features, and compare their performance on both seen and unseen data. We achieve improved results through combining these classifiers, showing that ensemble approaches are suitable when dealing with language-independent problems.