  The objective of few-shot named entity recognition is to identify named
entities with limited labeled instances. Previous works have primarily focused
on optimizing the traditional token-wise classification framework, while
neglecting the exploration of information based on NER data characteristics. To
address this issue, we propose a Multi-Task Semantic Decomposition Framework
via Joint Task-specific Pre-training (MSDP) for few-shot NER. Drawing
inspiration from demonstration-based and contrastive learning, we introduce two
novel pre-training tasks: Demonstration-based Masked Language Modeling (MLM)
and Class Contrastive Discrimination. These tasks effectively incorporate
entity boundary information and enhance entity representation in Pre-trained
Language Models (PLMs). In the downstream main task, we introduce a multi-task
joint optimization framework with the semantic decomposing method, which
facilitates the model to integrate two different semantic information for
entity classification. Experimental results of two few-shot NER benchmarks
demonstrate that MSDP consistently outperforms strong baselines by a large
margin. Extensive analyses validate the effectiveness and generalization of
MSDP.
