Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags

Understand

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.