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This paper introduces PERCEPT, the first large-scale Persian-English code-mixed corpus annotated with Universal Dependencies part-of-speech tags, addressing a significant gap in existing linguistic resources. The dataset, comprising 6,800 posts from social media platforms, enables detailed linguistic analysis and the development of syntax-aware NLP models for code-mixed language. Key findings highlight that nouns dominate code-mixed usage, with notable variations in part-of-speech distributions across platforms, particularly emphasizing the unique triggering effect observed in Digikala.
Code-mixed communication in Persian-English reveals that nouns dominate usage, challenging assumptions about language mixing in social media contexts.
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 X, Instagram, and Digikala. 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.