There is a recent interest in investigating few-shot NER, where the low-resource target
domain has different label sets compared with a resource-rich source domain. Existing …

Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low
resource domains. Existing approaches only learn class-specific semantic features and …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

Distance metric learning has become a popular solution for few-shot Named Entity
Recognition (NER). The typical setup aims to learn a similarity metric for measuring the …

This paper presents an empirical study to efficiently build named entity recognition (NER)
systems when a small amount of in-domain labeled data is available. Based upon recent …

Most NER methods rely on extensive labeled data for model training, which struggles in the
low-resource scenarios with limited training data. Existing dominant approaches usually …

This paper presents a comprehensive study to efficiently build named entity recognition
(NER) systems when a small number of in-domain labeled data is available. Based upon …

It is often challenging to solve a complex problem from scratch, but much easier if we can
access other similar problems with their solutions--a paradigm known as case-based …

We present an open-source and extensible knowledge extraction toolkit DeepKE,
supporting complicated low-resource, document-level and multimodal scenarios in the …

Few-shot learning arises in important practical scenarios, such as when a natural language
understanding system needs to learn new semantic labels for an emerging, resource-scarce …