Open Access

CODE-SWITCHED RELATION EXTRACTION: A NOVEL DATASET AND TRAINING METHODOLOGY

https://doi.org/10.55640/ijmcsit-v02i02-01

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Dr. Mingyu L. Chen ¹ , Muhammad Siddiqui ¹ ,

⁴ Department of Computer Science, Nanyang Technological University, Singapore

Abstract

Relation Extraction (RE) is a fundamental task in Natural Language Processing (NLP) crucial for constructing knowledge graphs and enhancing information retrieval. While significant progress has been made in monolingual and cross-lingual RE, the unique challenges posed by code-switched (mix-lingual) text remain largely underexplored due to a scarcity of dedicated datasets and tailored methodologies. This paper introduces a novel, large-scale dataset specifically designed for code-switched relation extraction. Furthermore, we propose an effective training methodology tailored to capture the complexities of inter- and intra-sentential code-switching phenomena. Our comprehensive experiments demonstrate that this new dataset and the proposed approach significantly advance the state-of-the-art in extracting relations from mix-lingual content, providing a valuable resource and benchmark for future research in this challenging domain.

Keywords

Code-switched text, relation extraction, multilingual NLP, dataset creation

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International Journal of Modern Computer Science and IT Innovations

CODE-SWITCHED RELATION EXTRACTION: A NOVEL DATASET AND TRAINING METHODOLOGY

Abstract

Keywords

References

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