# The Grammar and Syntax Based Corpus Analysis Tool For The Ukrainian Language

arXiv: https://arxiv.org/abs/2305.13530 · HTML: https://mlsanity.com/paper/2305.13530/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 6 of 6)

- [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://mlsanity.com/paper/2112.06598/) (2021)
- [dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python](https://mlsanity.com/paper/2012.14406/) (2020)
- [What does it mean to be language-agnostic? Probing multilingual sentence encoders for typological properties](https://mlsanity.com/paper/2009.12862/) (2020)
- [UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language](https://mlsanity.com/paper/2103.16997/) (2021)
- [LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa](https://mlsanity.com/paper/2305.03300/) (2023)
- [A Baseline Model for Software Effort Estimation](https://mlsanity.com/paper/2102.02933/) (2021)

## Built on by (most cited first, 1 of 1)

- [StyloMetrix: An Open-Source Multilingual Tool for Representing Stylometric Vectors](https://mlsanity.com/paper/2309.12810/) (2023)

## Abstract (arXiv)

This paper provides an overview of a text mining tool the StyloMetrix developed initially for the Polish language and further extended for English and recently for Ukrainian. The StyloMetrix is built upon various metrics crafted manually by computational linguists and researchers from literary studies to analyze grammatical, stylistic, and syntactic patterns. The idea of constructing the statistical evaluation of syntactic and grammar features is straightforward and familiar for the languages like English, Spanish, German, and others; it is yet to be developed for low-resource languages like Ukrainian. We describe the StyloMetrix pipeline and provide some experiments with this tool for the text classification task. We also describe our package's main limitations and the metrics' evaluation procedure.