# CaMEL: Case Marker Extraction without Labels

arXiv: https://arxiv.org/abs/2203.10010 · HTML: https://mlsanity.com/paper/2203.10010/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [UniMorph 2.0: Universal Morphology](https://mlsanity.com/paper/1810.11101/) (2018)
- [Cross-lingual, Character-Level Neural Morphological Tagging](https://mlsanity.com/paper/1708.09157/) (2017)
- [Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages](https://mlsanity.com/paper/1704.08914/) (2017)
- [Automatic Extraction of Rules Governing Morphological Agreement](https://mlsanity.com/paper/2010.01160/) (2020)
- [Neural Factor Graph Models for Cross-lingual Morphological Tagging](https://mlsanity.com/paper/1805.04570/) (2018)
- [Unsupervised Morphological Paradigm Completion](https://mlsanity.com/paper/2005.00970/) (2020)
- [Evaluating the Morphosyntactic Well-formedness of Generated Texts](https://mlsanity.com/paper/2103.16590/) (2021)
- [The Paradigm Discovery Problem](https://mlsanity.com/paper/2005.01630/) (2020)
- [The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion](https://mlsanity.com/paper/2005.13756/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Crosslingual Investigation of Conceptualization in 1335 Languages](https://mlsanity.com/paper/2305.08475/) (2023)
- [Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs](https://mlsanity.com/paper/2305.12818/) (2023)
- [Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model](https://mlsanity.com/paper/2310.15113/) (2023)

## Abstract (arXiv)

We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages. We propose a first model for CaMEL that uses a massively multilingual corpus to extract case markers in 83 languages based only on a noun phrase chunker and an alignment system. To evaluate CaMEL, we automatically construct a silver standard from UniMorph. The case markers extracted by our model can be used to detect and visualise similarities and differences between the case systems of different languages as well as to annotate fine-grained deep cases in languages in which they are not overtly marked.