  Data imbalance presents a significant challenge in various machine learning
(ML) tasks, particularly named entity recognition (NER) within natural language
processing (NLP). NER exhibits a data imbalance with a long-tail distribution,
featuring numerous minority classes (i.e., entity classes) and a single
majority class (i.e., O-class). This imbalance leads to misclassifications of
the entity classes as the O-class. To tackle this issue, we propose a simple
and effective learning method named majority or minority (MoM) learning. MoM
learning incorporates the loss computed only for samples whose ground truth is
the majority class into the loss of the conventional ML model. Evaluation
experiments on four NER datasets (Japanese and English) showed that MoM
learning improves prediction performance of the minority classes without
sacrificing the performance of the majority class and is more effective than
widely known and state-of-the-art methods. We also evaluated MoM learning using
frameworks as sequential labeling and machine reading comprehension, which are
commonly used in NER. Furthermore, MoM learning has achieved consistent
performance improvements regardless of language or framework.
