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Previous studies have shown that linguistic features of a word such aspossession, genitive or other grammatical cases can be employed in wordrepresentations of a named entity recognition (NER) tagger to improve theperformance for morphologically rich languages. However, these taggers requireexternal morphological disambiguation (MD) tools to function which are hard toobtain or non-existent for many languages. In this work, we propose a modelwhich alleviates the need for such disambiguators by jointly learning NER andMD taggers in languages for which one can provide a list of candidatemorphological analyses. We show that this can be done independent of themorphological annotation schemes, which differ among languages. Our experimentsemploying three different model architectures that join these two tasks showthat joint learning improves NER performance. Furthermore, the morphologicaldisambiguator's performance is shown to be competitive.

Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags