IITD at SemEval-2023 Task 2: A Multi-Stage Information Retrieval Approach for Fine-Grained Named Entity Recognition
Shivani Choudhary, Niladri Chatterjee, Subir Saha · 2023
MultiCoNER-II is a fine-grained Named Entity Recognition (NER) task that aims to identify ambiguous and complex named entities in multiple languages, with a small amount of contextual information available.To address this task, we propose a multi-stage information retrieval (IR) pipeline that improves the performance of language models for fine-grained NER.Our approach involves leveraging a combination of a BM25-based IR model and a language model to retrieve relevant passages from a corpus.These passages are then used to train a model that utilizes a weighted average of losses.The prediction is generated by a decoder stack that includes a projection layer and conditional random field.To demonstrate the effectiveness of our approach, we participated in the English track of the MultiCoNER-II competition.Our approach yielded promising results, which we validated through detailed analysis.