Relation extraction (RE) is a crucial task in natural language processing (NLP) that aims to identify and classify relationships between entities mentioned in text. In the financial domain, relation extraction plays a vital role in extracting valuable information from financial documents, such as news articles, earnings reports, and company filings. This paper describes our solution to relation extraction on one such dataset REFinD. The dataset was released along with shared task as a part of the Fourth Workshop on Knowledge Discovery from Unstructured Data in Financial Services, co-located with SIGIR 2023. In this paper, we employed OpenAI models under the framework of in-context learning (ICL). We utilized two retrieval strategies to find top K relevant in-context learning demonstrations / examples from training data for a given test example. The first retrieval mechanism, we employed, is a learning-free dense retriever and the other system is a learning-based retriever. We were able to achieve 4th rank on the leaderboard. Our best F1-score is 0.718.

翻译：关系抽取（RE）是自然语言处理（NLP）中的关键任务，旨在识别并分类文本中提及实体之间的关系。在金融领域，关系抽取对于从新闻文章、财报报告及公司备案等金融文档中提取有价值信息具有重要作用。本文描述了我们在REFinD数据集上针对关系抽取任务的解决方案。该数据集作为第四届金融服务非结构化数据知识发现研讨会（与SIGIR 2023合办）的共享任务而发布。我们采用上下文学习（ICL）框架下的OpenAI模型，并利用两种检索策略从训练数据中为给定测试样例选取最相关的K个上下文学习示例。第一种检索机制为无监督稠密检索器，第二种为基于学习的检索器。最终我们在排行榜上位列第四，最佳F1分数为0.718。