基于自动弱标注数据的跨领域命名实体识别

近年来,在大规模标注语料上训练的神经网络模型大大提升了命名实体识别任务的性能.但是,新领域人工标注数据获取代价高昂,如何快速、低成本地进行领域迁移就显得非常重要.在目标领域仅给定无标注数据的情况下,该文尝试自动构建目标领域的弱标注语料并对其建模.首先,采用两种不同的方法对无标注数据进行自动标注;然后,采用留"同"去"异...

Cross-Domain NER Using Automatically Partial-annotated Data

In recent years, neural network models trained on a large number of labeled samples boost the performance of named entity recognition. However, collecting enough labeled samples in various domains is very expensive, which reveals the importance of rapid domain transferring. Given only unlabeled data of a target domain, this paper attempts to automatically construct corpora with partial annotation in the target domain and model it. Firstly, we use two different methods to annotate unlabeled data automatically. Then, we keep consistent annotations while removing those with different annotations, which reduces erroneous annotations as much as possible and generates a partial-annotated corpus. Finally, we propose a new entity recognition model based on partial annotation learning. Experiment results of transferring from the news domain to the social-media domain as well as finance domain prove that the proposed approach effectively improves the domain adaptation performance of named entity recognition at a low cost. With the addition of pre-trained language model BERT, this method still exhibits good performance.