@inproceedings{ma-etal-2023-learning,
    title = "Learning {``}{O}{''} Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental {NER}",
    author = "Ma, Ruotian  and
      Chen, Xuanting  and
      Lin, Zhang  and
      Zhou, Xin  and
      Wang, Junzhe  and
      Gui, Tao  and
      Zhang, Qi  and
      Gao, Xiang  and
      Chen, Yun Wen",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.328",
    doi = "10.18653/v1/2023.acl-long.328",
    pages = "5959--5979",
    abstract = "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 learning for NER. Considering the privacy concerns and storage constraints, the standard paradigm for class-incremental NER updates the models with training data only annotated with the new classes, yet the entities from other entity classes are regarded as {``}Non-entity{''} (or {``}O{''}). In this work, we conduct an empirical study on the {``}Unlabeled Entity Problem{''} and find that it leads to severe confusion between {``}O{''} and entities, decreasing class discrimination of old classes and declining the model{'}s ability to learn new classes. To solve the Unlabeled Entity Problem, we propose a novel representation learning method to learn discriminative representations for the entity classes and {``}O{''}. Specifically, we propose an entity-aware contrastive learning method that adaptively detects entity clusters in {``}O{''}. Furthermore, we propose two effective distance-based relabeling strategies for better learning the old classes. We introduce a more realistic and challenging benchmark for class-incremental NER, and the proposed method achieves up to 10.62{\%} improvement over the baseline methods.",
}

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 learning for NER. Considering the privacy concerns and storage constraints, the standard paradigm for class-incremental NER updates the models with training data only annotated with the new classes, yet the entities from other entity classes are regarded as {``}Non-entity{''} (or {``}O{''}). In this work, we conduct an empirical study on the {``}Unlabeled Entity Problem{''} and find that it leads to severe confusion between {``}O{''} and entities, decreasing class discrimination of old classes and declining the model{'}s ability to learn new classes. To solve the Unlabeled Entity Problem, we propose a novel representation learning method to learn discriminative representations for the entity classes and {``}O{''}. Specifically, we propose an entity-aware contrastive learning method that adaptively detects entity clusters in {``}O{''}. Furthermore, we propose two effective distance-based relabeling strategies for better learning the old classes. We introduce a more realistic and challenging benchmark for class-incremental NER, and the proposed method achieves up to 10.62{\%} improvement over the baseline methods.
Ma, Ruotian Chen, Xuanting Lin, Zhang Zhou, Xin Wang, Junzhe Gui, Tao Zhang, Qi Gao, Xiang Chen, Yun Wen
Learning “O” Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NER