  Previous studies have shown that linguistic features of a word such as
possession, genitive or other grammatical cases can be employed in word
representations of a named entity recognition (NER) tagger to improve the
performance for morphologically rich languages. However, these taggers require
external morphological disambiguation (MD) tools to function which are hard to
obtain or non-existent for many languages. In this work, we propose a model
which alleviates the need for such disambiguators by jointly learning NER and
MD taggers in languages for which one can provide a list of candidate
morphological analyses. We show that this can be done independent of the
morphological annotation schemes, which differ among languages. Our experiments
employing three different model architectures that join these two tasks show
that joint learning improves NER performance. Furthermore, the morphological
disambiguator's performance is shown to be competitive.
