As the categories of named entities rapidly increase, the deployed NER models are required
to keep updating toward recognizing more entity types, creating a demand for class …

Incremental Named Entity Recognition (INER) aims to continually train a model with new
data, recognizing emerging entity types without forgetting previously learned ones. Prior …

In this work, we explore the way to quickly adjust an existing named entity recognition (NER)
system to make it capable of recognizing entity types not defined in the system. As an …

Previous work of class-incremental learning for Named Entity Recognition (NER) relies on
the assumption that there exists abundance of labeled data for the training of new classes. In …

Incremental Named Entity Recognition (INER) involves the sequential learning of new entity
types without accessing the training data of previously learned types. However, INER faces …

Recently, many studies have illustrated the robustness problem of Named Entity
Recognition (NER) systems: the NER models often rely on superficial entity patterns for …

In this paper, we propose to use a sequence to sequence model for Named Entity
Recognition (NER) and we explore the effectiveness of such model in a progressive NER …

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 …

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 …

Supervised named entity recognition (NER) aims to classify entity mentions into a fixed
number of pre-defined types. However, in real-world scenarios, unknown entity types are …