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 framework 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.

本文提出了一种新的命名实体识别（NER）技术，专门针对开源软件系统。我们的方法通过使用全面的两步远程监督注释过程来解决注释软件数据的稀缺性问题。该方法战略性地利用语言启发式方法、唯一的查找表、外部知识源和主动学习方法。通过利用这些强大的技术，我们不仅提高了模型的性能，还有效地克服了成本和专家注释者的稀缺性方面的局限性。值得注意的是，我们的框架在很大程度上超越了现有技术的领先水平。我们还展示了 NER 在下游关系抽取任务中的有效性。