Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Morphological Embeddings for Named Entity Recognition in Morphologically
  Rich Languages   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.

18
The categories of this paper are: econ
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph