Comparative Study of Zero-Shot Cross-Lingual Transfer for Bodo POS and NER Tagging Using Gemini 2.0 Flash Thinking Experimental Model

Part-of-Speech (POS) tagging and Named Entity Recognition (NER) are fundamental tasks within the field of Natural Language Processing (NLP), serving as essential prerequisites for a multitude of downstream applications. POS tagging, the process of assigning grammatical categories to individual words within a sentence (e.g., noun, verb, adjective, adverb), provides crucial syntactic information that underpins higher-level language understanding. NER, on the contrary, focuses on identifying and classifying named entities - real-world objects that are designated with a proper name - into predefined semantic categories such as persons, organizations, locations, dates, times, and quantities [1, 2]. The synergy of POS and NER tagging empowers a wide spectrum of NLP applications. In information extraction, NER helps to pinpoint key entities, while POS tags help to understand the relationships between these entities and other words in the text, facilitating the extraction of structured information from unstructured text [3]. Machine translation systems benefit from POS tagging to improve syntactic analysis and word order prediction, and NER to ensure accurate translation of named entities in languages [4]. Question-answer systems rely on both NER and POS to understand the question's intent, identify relevant entities and relationships in the knowledge base, and formulate accurate answers. Text summarization algorithms leverage NER to identify salient entities and POS tags to preserve grammatical coherence and readability in summaries.