【摘要】 地理命名实体识别是高质量地理知识图谱构建的重要环节，被广泛应用于地理编码、语义检索及地理知识推理等方面。主流的深度学习模型存在标注语料库耗时费力、模型可解释性差等问题。为发挥人在回路机制推动学习模型利用少量样本学习的优势，本文提出了一种人在回路学习增强的地理命名实体识别方法。即以部分标注及未标注地理语料为输入，基于BERT-BiLSTM-CRF模型进行训练并对待标注语料库进行识别，对于模型识别错误的句子提供人工干预形式对其进行纠正，并将纠正之后的句子重新输送到学习模型中进行迭代训练，最终形成标准地理命名实体数据集及人在回路强化后的抽取模型。以地理大百科全书数据为例进行模型性能评估，该方法对于多数地理命名实体识别解析准确率达90%以上，相比已有深度学习模型，该方法仅需要少量标注样本且识别效果更优，对多种地理命名实体识别类型能够保持较好性能。更多还原

【Abstract】 Geographical named entity recognition is an important part of high-quality geographic knowledge graph construction, which is widely used in geographic coding, semantic retrieval and geographic knowledge inference. The mainstream deep learning models suffer from the problems of time-consuming and laborious annotation corpus and poor model interpretability. In order to take advantage of the human-in-the-loop mechanism to promote learning models using a small number of samples, a geographical named entity recognition method based on human-in-the-loop learning enhancement is proposed: partially labeled and unlabeled geographic corpus is used as input, trained based on BERT-BiLSTM-CRF model and recognized to the labeled corpus, and the sentences that are incorrectly recognized by the model are provided with human intervention in the form of the corrected sentences are re-transported to the learning model for training again; after several iterations, the standard geographic named entity dataset and the human extraction model after loop reinforcement are finally formed. The performance of the model is evaluated using the geographic encyclopedia data as an example, and the accuracy of the method is over 90% for most of the geographical named entity recognition parses.更多还原