Learning “O” Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NER

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 5959–5979 July 9-14, 2023 ©2023 Association for Computational Linguistics Learning "O" Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NER Ruotian Ma1∗, Xuanting Chen1∗, Lin Zhang1, Xin Zhou1, Junzhe Wang1, Tao Gui2†, Qi Zhang1†, Xiang Gao3, Yunwen Chen3 1School of Computer Science, Fudan University, Shanghai, China 2Institute of Modern Languages and Linguistics, Fudan University, Shanghai, China 3DataGrand Information Technology (Shanghai) Co., Ltd. {rtma19,xuantingchen21,tgui,qz}@fudan.edu.cn Abstract As the categories of named entities rapidly increase, the deployed NER models are re- quired 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 enti- ties from other entity classes are unlabeled, re- garded as "Non-entity" (or "O"). In this work, we conduct an empirical study on the "Unla- beled 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 Prob- lem, 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. 1 Introduction Existing Named Entity Recognition systems are typically trained on a large-scale dataset with pre- defined entity classes, then deployed for entity recognition on the test data without further adap- tation or refinement (Li et al., 2020; Wang et al., 2022; Liu et al., 2021; Ma et al., 2022a). In prac- tice, the newly-arriving test data may include new entity classes, and the user’s required entity class set might keep expanding. Therefore, it is in de- mand that the NER model can be incrementally ∗Equal contribution. †Corresponding authors. "O" ... ... LOC Step 1 Step 2 PER LOC FILM Interstellar , directed by Nolan, premiered on in 2014 in California. PER Interstellar , directed by Nolan, premiered on in 2014 in California. "O" "O" "O" "O" ... ... Step K "O" Interstellar , directed by Nolan, premiered on in 2014 in California. FILM "O" DATE "O" DATE ... "O" "O" "O" Potential class Old class Step 1 Step 2 Step K ... ... Figure 1: Problems of class-incremental NER. In each incremental step, the data is only labeled with current classes, so the "O" class actually contains entities from old classes and entities from potential classes. updated for recognizing new entity classes. How- ever, one challenge is that the training data of old entity classes may not be available due to privacy concerns …

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