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Gemini 2.0 Flash Thinking's Zero-Shot Cross-Lingual NER Robustness to Domain Shifts in Low-Resource Languages

Domain:

natural language processing

Record type:

paper
Creator:
Ass
Publisher:
Zenodo
Host:avatar
Named Entity Recognition (NER) and Part-of-Speech (POS) tagging are critical tasks for Natural Language Processing (NLP), yet their availability for low-resource languages (LRLs) like Bodo remains limited. This article presents a comparative empirical study investigating the effectiveness of Google's Gemini 2.0 Flash Thinking Experiment model for zero-shot cross-lingual transfer of POS and NER tagging to Bodo. We explore two distinct methodologies: (1) direct translation of English sentences to Bodo followed by tag transfer, and (2) prompt-based tag transfer on parallel English-Bodo sentence p Research goal: How robust is Gemini 2.0 Flash Thinking's zero-shot cross-lingual NER performance to domain shifts (e.g., news vs. social media text) in low-resource languages, as measured by F1 score differences? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.5/10.

Visit

doi.orgzenodo.org

Tasks

named entity recognitioninformation extraction

Languages

Bodo

Tags

robustGeminiFlashThinkingzero-shotcross-lingualNERperformance

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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