# Morfessor EM+Prune: Improved Subword Segmentation with Expectation Maximization and Pruning

arXiv: https://arxiv.org/abs/2003.03131 · HTML: https://mlsanity.com/paper/2003.03131/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [The SIGMORPHON 2022 Shared Task on Morpheme Segmentation](https://mlsanity.com/paper/2206.07615/) (2022)
- [Improving Tokenisation by Alternative Treatment of Spaces](https://mlsanity.com/paper/2204.04058/) (2022)
- [Effects of sub-word segmentation on performance of transformer language models](https://mlsanity.com/paper/2305.05480/) (2023)
- [MorphPiece : A Linguistic Tokenizer for Large Language Models](https://mlsanity.com/paper/2307.07262/) (2023)
- [Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text](https://mlsanity.com/paper/2210.06990/) (2022)
- [The Current State of Finnish NLP](https://mlsanity.com/paper/2109.11326/) (2021)
- [Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition](https://mlsanity.com/paper/2103.07186/) (2021)
- [Revisiting subword tokenization: A case study on affixal negation in large language models](https://mlsanity.com/paper/2404.02421/) (2024)
- [chDzDT: Word-level morphology-aware language model for Algerian social media text](https://mlsanity.com/paper/2509.01772/) (2025)
- [Unsupervised Morphological Tree Tokenizer](https://mlsanity.com/paper/2406.15245/) (2024)

## Abstract (arXiv)

Data-driven segmentation of words into subword units has been used in various natural language processing applications such as automatic speech recognition and statistical machine translation for almost 20 years. Recently it has became more widely adopted, as models based on deep neural networks often benefit from subword units even for morphologically simpler languages. In this paper, we discuss and compare training algorithms for a unigram subword model, based on the Expectation Maximization algorithm and lexicon pruning. Using English, Finnish, North Sami, and Turkish data sets, we show that this approach is able to find better solutions to the optimization problem defined by the Morfessor Baseline model than its original recursive training algorithm. The improved optimization also leads to higher morphological segmentation accuracy when compared to a linguistic gold standard. We publish implementations of the new algorithms in the widely-used Morfessor software package.