Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages

06/01/2017

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by   Onur Gungor, et al.

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