# Canonical and Surface Morphological Segmentation for Nguni Languages

arXiv: https://arxiv.org/abs/2104.00767 · HTML: https://mlsanity.com/paper/2104.00767/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Cross-lingual Word Segmentation and Morpheme Segmentation as Sequence Labelling](https://mlsanity.com/paper/1709.03756/) (2017)

## Built on by (most cited first, 10 of 10)

- [A Masked Segmental Language Model for Unsupervised Natural Language Segmentation](https://mlsanity.com/paper/2104.07829/) (2021)
- [Subword Segmental Language Modelling for Nguni Languages](https://mlsanity.com/paper/2210.06525/) (2022)
- [Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation](https://mlsanity.com/paper/2305.07005/) (2023)
- [Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation](https://mlsanity.com/paper/2205.08621/) (2022)
- [Morphological Processing of Low-Resource Languages: Where We Are and What's Next](https://mlsanity.com/paper/2203.08909/) (2022)
- [Feature-based analysis of oral narratives from Afrikaans and isiXhosa children](https://mlsanity.com/paper/2507.13164/) (2025)
- [Automatically assessing oral narratives of Afrikaans and isiXhosa children](https://mlsanity.com/paper/2507.13205/) (2025)
- [The evaluation of a code-switched Sepedi-English automatic speech recognition system](https://mlsanity.com/paper/2403.07947/) (2024)
- [TAMS: Translation-Assisted Morphological Segmentation](https://mlsanity.com/paper/2403.14840/) (2024)
- [Neural Morphological Tagging for Nguni Languages](https://mlsanity.com/paper/2505.12949/) (2025)

## Abstract (arXiv)

Morphological Segmentation involves decomposing words into morphemes, the smallest meaning-bearing units of language. This is an important NLP task for morphologically-rich agglutinative languages such as the Southern African Nguni language group. In this paper, we investigate supervised and unsupervised models for two variants of morphological segmentation: canonical and surface segmentation. We train sequence-to-sequence models for canonical segmentation, where the underlying morphemes may not be equal to the surface form of the word, and Conditional Random Fields (CRF) for surface segmentation. Transformers outperform LSTMs with attention on canonical segmentation, obtaining an average F1 score of 72.5% across 4 languages. Feature-based CRFs outperform bidirectional LSTM-CRFs to obtain an average of 97.1% F1 on surface segmentation. In the unsupervised setting, an entropy-based approach using a character-level LSTM language model fails to outperforms a Morfessor baseline, while on some of the languages neither approach performs much better than a random baseline. We hope that the high performance of the supervised segmentation models will help to facilitate the development of better NLP tools for Nguni languages.