  We propose a morphologically informed model for named entity recognition,
which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM
character embeddings, part-of-speech (POS) tags, and morphological information.
While previous work has focused on learning from raw word input, using word and
character embeddings only, we show that for morphologically rich languages,
such as Bulgarian, access to POS information contributes more to the
performance gains than the detailed morphological information. Thus, we show
that named entity recognition needs only coarse-grained POS tags, but at the
same time it can benefit from simultaneously using some POS information of
different granularity. Our evaluation results over a standard dataset show
sizable improvements over the state-of-the-art for Bulgarian NER.
