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We consider the problem of disambiguating the lemma and part of speech ofambiguous words in morphologically rich languages. We propose a method fordisambiguating ambiguous words in context, using a large un-annotated corpus oftext, and a morphological analyser -- with no manual disambiguation or dataannotation. We assume that the morphological analyser produces multipleanalyses for ambiguous words. The idea is to train recurrent neural networks onthe output that the morphological analyser produces for unambiguous words. Wepresent performance on POS and lemma disambiguation that reaches or surpassesthe state of the art -- including supervised models -- using no manuallyannotated data. We evaluate the method on several morphologically richlanguages.

Neural disambiguation of lemma and part of speech in morphologically rich languages