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LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa

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arxiv 2305.03300 v1 pith:YCFYOLDI submitted 2023-05-05 cs.CL

Rahul Mehta , Vasudeva Varma This is my paper · ORCID
classification cs.CL
keywords taskcomplexmultilingualnamedentitiesentityllm-rmprovided
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Named Entity Recognition(NER) is a task of recognizing entities at a token level in a sentence. This paper focuses on solving NER tasks in a multilingual setting for complex named entities. Our team, LLM-RM participated in the recently organized SemEval 2023 task, Task 2: MultiCoNER II,Multilingual Complex Named Entity Recognition. We approach the problem by leveraging cross-lingual representation provided by fine-tuning XLM-Roberta base model on datasets of all of the 12 languages provided -- Bangla, Chinese, English, Farsi, French, German, Hindi, Italian, Portuguese, Spanish, Swedish and Ukrainian

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  1. FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Framework

    cs.CL 2025-02 conditional novelty 5.0 of 10

    FewTopNER reports that adding a topic-modeling branch to a prototype-based few-shot NER model improves multilingual F1 by 2.5 to 4.0 points and increases topic coherence scores.

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