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arXiv · 1706.00506

Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages

Onur Gungor·Eray Yildiz·Suzan Uskudarli·Tunga Gungor
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Abstract

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 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.

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Onur Gungor, Eray Yildiz, Suzan Uskudarli, Tunga Gungor. 2017-06-01. Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages. https://arxiv.org/abs/1706.00506

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