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

arXiv: https://arxiv.org/abs/2007.06104 · HTML: https://mlsanity.com/paper/2007.06104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered](https://mlsanity.com/paper/2105.12428/) (2021)

## Abstract (arXiv)

We consider the problem of disambiguating the lemma and part of speech of ambiguous words in morphologically rich languages. We propose a method for disambiguating ambiguous words in context, using a large un-annotated corpus of text, and a morphological analyser -- with no manual disambiguation or data annotation. We assume that the morphological analyser produces multiple analyses for ambiguous words. The idea is to train recurrent neural networks on the output that the morphological analyser produces for unambiguous words. We present performance on POS and lemma disambiguation that reaches or surpasses the state of the art -- including supervised models -- using no manually annotated data. We evaluate the method on several morphologically rich languages.