PulseAugur
EN
LIVE 00:01:25

New corpus PERCEPT enables analysis of Persian-English code-mixing

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced PERCEPT, the first large-scale corpus for analyzing Persian-English code-mixing, specifically targeting part-of-speech (POS) tagging for code-mixed words. This new dataset, comprising 6,800 posts from platforms like X, Instagram, and Digikala, includes Universal Dependencies POS annotations. An LLM-assisted framework was employed for annotation, showing high agreement with human evaluations, which validates its reliability. The corpus enables the first comprehensive linguistic analysis of Persian-English code-mixing, revealing that nouns are the most frequent code-mixed word category and that code-mixed words appear consistently across platforms, with a stronger triggering effect observed on Digikala. AI

IMPACT Enables development of NLP models for code-mixed languages and deeper linguistic understanding of multilingual social media communication.

RANK_REASON The item describes a new corpus and associated analysis for a specific linguistic phenomenon, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New corpus PERCEPT enables analysis of Persian-English code-mixing

How we ranked this

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
The item describes a new corpus and associated analysis for a specific linguistic phenomenon, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
59 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ghazal Kalhor, Zahra Jafari, Amirarsalan Shahbazi, Behnam Bahrak ·

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

    arXiv:2608.10109v1 Announce Type: new Abstract: 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-Engli…

RELATED ENTITIES

RELATED TOPICS