论文标题
具有结构化最近的邻居学习
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning
论文作者
论文摘要
我们基于最近的邻居学习和结构化推理，提出了一个简单的几弹性命名实体识别（NER）系统。我们的系统使用在源域上训练的监督NER模型作为功能提取器。在几个测试域中，我们表明此功能空间中最近的邻居分类器比标准的元学习方法更有效。我们进一步提出了一种廉价但有效的方法，可以在没有昂贵的CRF培训的情况下捕获实体标签之间的标签依赖性。我们表明，将结构化解码与最近的邻居学习相结合的方法在标准的几次NER评估任务上实现了最先进的表现，将F1分数提高了$ 6 \％$ $ $ $ $ $ $ 16 \％\％\％$ $绝对点比先前的基于元学习的系统。
We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, as a feature extractor. Across several test domains, we show that a nearest neighbor classifier in this feature-space is far more effective than the standard meta-learning approaches. We further propose a cheap but effective method to capture the label dependencies between entity tags without expensive CRF training. We show that our method of combining structured decoding with nearest neighbor learning achieves state-of-the-art performance on standard few-shot NER evaluation tasks, improving F1 scores by $6\%$ to $16\%$ absolute points over prior meta-learning based systems.
