LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa
Rahul Mehta, Vasudeva Varma
Abstract
Named Entity Recognition(NER) is a task ofrecognizing entities at a token level in a sen-tence. This paper focuses on solving NER tasksin a multilingual setting for complex named en-tities.Our team, LLM-RM participated in therecently organized SemEval 2023 task, Task 2:MultiCoNER II,Multilingual Complex NamedEntity Recognition. We approach the problemby leveraging cross-lingual representation pro-vided by fine-tuning XLM-Roberta base modelon datasets of all of the 12 languages provided - Bangla, Chinese, English, Farsi, French,German, Hindi, Italian, Portuguese, Spanish,Swedish and Ukrainian.
Topics & Concepts
Named-entity recognitionSemEvalComputer scienceNatural language processingTask (project management)GermanArtificial intelligenceSecurity tokenPortugueseLinguisticsPhilosophyEconomicsComputer securityManagementTopic ModelingNatural Language Processing TechniquesData Quality and Management