# Labeled Morphological Segmentation with Semi-Markov Models

arXiv: https://arxiv.org/abs/2404.08997 · HTML: https://mlsanity.com/paper/2404.08997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 42 references that are in the map.
- First posted on arXiv 13 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation](https://mlsanity.com/paper/2404.09371/) (2024)
- [Quantifying Phonosemantic Iconicity Distributionally in 6 Languages](https://mlsanity.com/paper/2510.14040/) (2025)

## Abstract (arXiv)

We present labeled morphological segmentation, an alternative view of morphological processing that unifies several tasks. From an annotation standpoint, we additionally introduce a new hierarchy of morphotactic tagsets. Finally, we develop modelname, a discriminative morphological segmentation system that, contrary to previous work, explicitly models morphotactics. We show that textscchipmunk yields improved performance on three tasks for all six languages: (i) morphological segmentation, (ii) stemming and (iii) morphological tag classification. On morphological segmentation, our method shows absolute improvements of 2--6 points F1 over the baseline.