关键词: 主动学习, 知识抽取, 命名实体识别, 关系抽取, 人机交互

Abstract: Unstructured text data contains a large amount of valuable knowledge,entities and relations extracted from which can form structured knowledge and help to build knowledge graphs and support downstream tasks.There is a wide range of application prospects for entity and relation extraction.Currently,entity and relation extraction mostly use deep learning methods.However,the training of deep learning models consumes large amounts of annotated datasets,resulting in high labor cost.Therefore,how to reduce the workload of manual annotation is one of the focuses of research.Active learning is a subfield of machine lear-ning,which aims to maximize a model's performance gain while annotating the fewest samples possible,by selecting the most va-luable samples to be labeled and handed over to the model for training.Its potential to reduce training data complements the data-hungry nature of deep learning.Therefore,deep active learning that applies active learning in deep learning has become a hot research topic in entity and relation extraction.In the above context,using deep active learning for joint entity and relation extraction and appling active learning to the training process of the deep learning model to minimize the manual labeled data required for training while maintaining model performance,a deep learning model based on unified label space and matrix annotation for entity relation joint extraction is implemented and based on it,a variety of active learning query strategies are designed and implemented.The validity of the method is verified on text datasets and common entity and relation joint extraction datasets in the medical field.Several methods are proposed to select the stopping time of model training,including methods based on training loss curve of the model,model performance on the training set,and the prediction stability on reserved data.The method of selecting stop time for practical application scenario is studied by experiments.An intelligent text annotation tool based on active learning for joint extraction of entity and relation is designed and implemented,which allows users to annotate entities and relations in the text.The tool implements a deep learning model for entity and relation extraction and active learning methods to minimize the annotation workload of users.

Key words: Active learning, Knowledge extraction, Named entity recognition, Relation extraction, Human-machine interaction