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不同领域专业人员（如开源软件系统、医疗系统、银行系统、交通系统等）的趋势日益显著。此类系统自动化辅助工具的关键需求在于早期识别命名实体，这是开发专业功能的基础。然而，由于每个领域的特定性质、不同的技术术语和专用语言，对可用数据进行专家标注变得昂贵且具有挑战性。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（NER）技术。我们的方法通过采用全面的两步远程监督标注流程，旨在解决标注软件数据稀缺的问题。该流程策略性地利用语言启发规则、专属查找表、外部知识源以及主动学习方法。通过整合这些强大技术，我们不仅提升了模型性能，还有效缓解了成本限制和专家标注员稀缺的问题。值得注意的是，我们的模型显著超越了当前最先进的大语言模型（LLMs），并展示了NER在下游关系抽取任务中的有效性。