# PERCEPT: A Corpus for POS Tagging and Analysis of Persian-English Code-Mixing

arXiv: https://arxiv.org/abs/2608.10109 · HTML: https://mlsanity.com/paper/2608.10109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 10 Aug 2026.

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

- [A Sentiment Analysis Dataset for Code-Mixed Malayalam-English](https://mlsanity.com/paper/2006.00210/) (2020)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis](https://mlsanity.com/paper/2408.08964/) (2024)
- [COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing](https://mlsanity.com/paper/2503.21670/) (2025)
- [Sentiment Analysis of Persian-English Code-mixed Texts](https://mlsanity.com/paper/2102.12700/) (2021)
- [Automatic Normalization of Word Variations in Code-Mixed Social Media Text](https://mlsanity.com/paper/1804.00804/) (2018)

## Abstract (arXiv)

Social media has become a major venue for multilingual communication, where users frequently mix multiple languages within a single utterance. Although code-mixed corpora have been developed for several language pairs, Persian-English code-mixing remains relatively underexplored. Existing Persian resources lack Universal Dependencies (UD) part-of-speech (POS) annotations for code-mixed words, limiting both linguistic analyses and the development of syntax-aware NLP models. To address this gap, we introduce PERCEPT, the first publicly available large-scale Persian-English code-mixed corpus annotated with Universal Dependencies POS tags for code-mixed words. The dataset comprises 6,800 posts collected from Twitter (X), Instagram, and Digikala, each containing at least one identified Persian-English code-mixed word. We further present an LLM-assisted annotation framework that automatically assigns POS tags and document-level topics. Human evaluation demonstrates high agreement between the automatically generated annotations and gold annotations, confirming the reliability of the annotations. Using PERCEPT, we conduct the first comprehensive linguistic analysis of Persian-English code-mixing across multiple social media platforms. Our analyses reveal that nouns are the predominant category for code-mixed words, while the distributions of other POS categories vary across platforms. We further find that the positional distribution of code-mixed words is remarkably consistent across platforms, whereas the triggering effect is substantially more pronounced in Digikala. PERCEPT is publicly available at https://github.com/kalhorghazal/PERCEPT.