With the AI revolution in place, the trend for building automated systems to support professionals in different domains such as the open source software systems, healthcare systems, banking systems, transportation systems and many others have become increasingly prominent. A crucial requirement in the automation of support tools for such systems is the early identification of named entities, which serves as a foundation for developing specialized functionalities. However, due to the specific nature of each domain, different technical terminologies and specialized languages, expert annotation of available data becomes expensive and challenging. In light of these challenges, this paper proposes a novel named entity recognition (NER) technique specifically tailored for the open-source software systems. Our approach aims to address the scarcity of annotated software data by employing a comprehensive two-step distantly supervised annotation process. This process strategically leverages language heuristics, unique lookup tables, external knowledge sources, and an active learning approach. By harnessing these powerful techniques, we not only enhance model performance but also effectively mitigate the limitations associated with cost and the scarcity of expert annotators. It is noteworthy that our model significantly outperforms the state-of-the-art LLMs by a substantial margin. We also show the effectiveness of NER in the downstream task of relation extraction.

翻译：随着人工智能革命的推进，构建自动化系统以支持开源软件系统、医疗系统、银行系统、交通系统等不同领域专业人士的趋势日益显著。此类系统支持工具自动化的关键需求在于对命名实体的早期识别，这是开发专业化功能的基础。然而，由于各领域的具体特性、不同的技术术语和专门语言，对现有数据进行专家标注既昂贵又具有挑战性。针对这些挑战，本文提出了一种专门针对开源软件系统的新型命名实体识别技术。我们的方法通过采用全面的两步远程监督标注流程，旨在解决软件标注数据稀缺的问题。该流程策略性地结合了语言启发式规则、专用查找表、外部知识源以及主动学习方法。通过利用这些强大技术，我们不仅提升了模型性能，还有效缓解了成本高昂与专家标注员稀缺相关的限制。值得注意的是，我们的模型以显著优势超越了当前最先进的大型语言模型。我们还展示了命名实体识别在下游关系抽取任务中的有效性。