Abstract
In recent years, with the appearance of the COVID-19 pandemic, numerous publications relevant to this disease have been issued. Because of the massive volume of publications, an efficient retrieval system is necessary to provide researchers with useful information if an unexpected pandemic happens so suddenly, like COVID-19. In this work, we present a method to help the retrieval system, the Covrelex-SE system, to provide more high-quality search results. We exploited the power of the large language models (LLMs) to extract the hidden relationships inside the unlabeled publication that cannot be found by the current parsing tools that the system is using. Since then, help the system to have more useful information during retrieval progress.
Abstract (translated)
近年来，随着COVID-19大流行病的出现，与该疾病相关的大量出版物相继问世。鉴于这些出版物的数量庞大，一个高效的检索系统对于在类似突如其来的疫情（如COVID-19）发生时为研究人员提供有用信息是必不可少的。在这项工作中，我们提出了一种方法来帮助检索系统——Covrelex-SE系统，以提供更高质量的搜索结果。我们利用大型语言模型（LLMs）的力量，从当前系统使用的解析工具无法发现的未标注出版物中提取隐藏的关系。通过这种方式，可以为系统的检索过程提供更多有用的信息。
URL
https://arxiv.org/abs/2506.18311
