Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages
Onur Güngör, Eray Yıldız, Suzan Üsküdarlı, Tunga Güngör · arXiv (Cornell University) · 2017
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.