论文标题
标签语义的少数镜头名称实体识别
Label Semantics for Few Shot Named Entity Recognition
论文作者
论文摘要
我们研究了命名实体识别的射击学习的问题。具体来说，我们利用标签名称来利用语义信息，作为给模型额外信号和丰富的先验的一种方式。我们提出了一个神经体系结构，该神经体系结构由两个Bert编码器组成，一个用于编码文档及其令牌，另一个用于以自然语言格式编码每个标签。我们的模型学会了将第一个编码器计算出的指定实体的表示与第二个编码器计算的标签表示。显示标签语义信号可支持改进的最先进的信号，从而在标准基准测试中获得了多个Shot NER基准和PAR性能。我们的模型在低资源设置中特别有效。
We study the problem of few shot learning for named entity recognition. Specifically, we leverage the semantic information in the names of the labels as a way of giving the model additional signal and enriched priors. We propose a neural architecture that consists of two BERT encoders, one to encode the document and its tokens and another one to encode each of the labels in natural language format. Our model learns to match the representations of named entities computed by the first encoder with label representations computed by the second encoder. The label semantics signal is shown to support improved state-of-the-art results in multiple few shot NER benchmarks and on-par performance in standard benchmarks. Our model is especially effective in low resource settings.
