This paper describes our participation in the Shared Task on Software
Mentions Disambiguation (SOMD), with a focus on improving relation extraction
in scholarly texts through Generative Language Models (GLMs) using
single-choice question-answering. The methodology prioritises the use of
in-context learning capabilities of GLMs to extract software-related entities
and their descriptive attributes, such as distributive information. Our
approach uses Retrieval-Augmented Generation (RAG) techniques and GLMs for
Named Entity Recognition (NER) and Attributive NER to identify relationships
between extracted software entities, providing a structured solution for
analysing software citations in academic literature. The paper provides a
detailed description of our approach, demonstrating how using GLMs in a
single-choice QA paradigm can greatly enhance IE methodologies. Our
participation in the SOMD shared task highlights the importance of precise
software citation practices and showcases our system's ability to overcome the
challenges of disambiguating and extracting relationships between software
mentions. This sets the groundwork for future research and development in this
field.

本文描述了我们在软件提及消岐（SOMD）共享任务中的参与，重点是通过使用生成式语言模型（GLMs）和单选题答题来改善学术文本中的关系抽取。我们的方法优先使用 GLMs 的上下文学习能力，提取与软件相关的实体及其描述属性，例如分布信息。我们的方法使用了检索辅助生成（RAG）技术和 GLMs 进行命名实体识别（NER）和属性 NER，以识别提取的软件实体之间的关系，为分析学术文献中的软件引用提供了结构化解决方案。本文详细描述了我们的方法，并展示了在单选题答题范式中使用 GLMs 可以极大地增强 IE 方法的能力。我们在 SOMD 共享任务中的参与强调了精确软件引用实践的重要性，并展示了我们的系统在消除提及歧义和提取软件关系方面的能力，为该领域的未来研究和发展奠定了基础。