论文标题
通过检索非结构化知识，自适应命名实体识别
Self-Adaptive Named Entity Recognition by Retrieving Unstructured Knowledge
论文作者
论文摘要
尽管命名的实体识别（NER）可以帮助我们从文本中提取特定领域的实体（例如，音乐领域中的艺术家），但创建大量培训数据或结构化知识库以在目标域中执行准确的NER是昂贵的。在这里，我们提出了自适应的NER，该NER从非结构化的文本中检索了外部知识，以了解尚未学会的实体的用法。为了检索NER的有用知识，我们设计了一个有效的两阶段模型，该模型使用不确定的实体作为查询来检索非结构化知识。我们的模型预测了输入中的实体，然后发现预测不自信的实体。然后，它通过使用这些不确定的实体作为查询来检索知识，并将检索到的文本与原始输入串联以修改预测。在Crossner数据集上的实验表明，我们的模型在F1公制中的表现优于强大基线2.35点。
Although named entity recognition (NER) helps us to extract domain-specific entities from text (e.g., artists in the music domain), it is costly to create a large amount of training data or a structured knowledge base to perform accurate NER in the target domain. Here, we propose self-adaptive NER, which retrieves external knowledge from unstructured text to learn the usages of entities that have not been learned well. To retrieve useful knowledge for NER, we design an effective two-stage model that retrieves unstructured knowledge using uncertain entities as queries. Our model predicts the entities in the input and then finds those of which the prediction is not confident. Then, it retrieves knowledge by using these uncertain entities as queries and concatenates the retrieved text to the original input to revise the prediction. Experiments on CrossNER datasets demonstrated that our model outperforms strong baselines by 2.35 points in F1 metric.
