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深入推进，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员的趋势日益显著。为此类系统开发自动化支持工具的关键前提在于实现命名实体的早期识别，这构成了开发专业化功能的基础。然而，由于各领域特有的技术术语与专业语言差异，对现有数据进行专家标注成本高昂且极具挑战。针对这些问题，本文提出一种专为开源软件系统设计的新型命名实体识别技术。该方法通过采用包含两个步骤的远程监督标注流程，旨在缓解软件标注数据稀缺的困境。该流程策略性地融合了语言启发式规则、定制化查找表、外部知识源以及主动学习方法。借助这些技术，我们不仅提升了模型性能，还有效克服了标注成本与专家资源稀缺的限制。值得注意的是，本模型以显著优势超越了当前最先进的大型语言模型。我们还验证了命名实体识别在关系抽取下游任务中的有效性。