Few-Shot Named Entity Recognition with the Integration of Spatial Features

The few-shot named entity recognition (NER) task aims to train a robust model in the source domain and transfer it to the target domain with very few annotated data. Currently, some approaches rely on the prototypical network for NER. However, these approaches often overlook the spatial relations in the span boundary matrix because entity words tend to depend more on adjacent words. We propose using a multidimensional convolution module to address this limitation to capture short-distance spatial dependencies. Additionally, we utilize an improved prototypical network and assign different weights to different samples that belong to the same class, thereby enhancing the performance of the few-shot NER task. Further experimental analysis demonstrates that our approach has significantly improved over baseline models across multiple datasets.