# An Unsupervised Method for Uncovering Morphological Chains

arXiv: https://arxiv.org/abs/1503.02335 · HTML: https://mlsanity.com/paper/1503.02335/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2015.

## Built on by (most cited first, 8 of 8)

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages](https://mlsanity.com/paper/2203.08954/) (2022)
- [Compositionality as Lexical Symmetry](https://mlsanity.com/paper/2201.12926/) (2022)
- [Morphological Processing of Low-Resource Languages: Where We Are and What's Next](https://mlsanity.com/paper/2203.08909/) (2022)
- [Morpheme Boundary Detection & Grammatical Feature Prediction for Gujarati : Dataset & Model](https://mlsanity.com/paper/2112.09860/) (2021)
- [Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention](https://mlsanity.com/paper/2102.01223/) (2021)
- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [Minimal Supervision for Morphological Inflection](https://mlsanity.com/paper/2104.08512/) (2021)

## Abstract (arXiv)

Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations. We use log-linear models with morpheme and word-level features to predict possible parents, including their modifications, for each word. The limited set of candidate parents for each word render contrastive estimation feasible. Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.