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In this work, we present new state-of-the-art results of 93.59,% and 79.59,%for Turkish and Czech named entity recognition based on the model of (Lample etal., 2016). We contribute by proposing several schemes for representing themorphological analysis of a word in the context of named entity recognition. Weshow that a concatenation of this representation with the word and characterembeddings improves the performance. The effect of these representation schemeson the tagging performance is also investigated.

Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages