Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition

arXiv:2401.11431v2 Announce Type: replace
Abstract: 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. …

abstract arxiv challenge class cs.cl data distribution language language processing machine machine learning natural natural language natural language processing ner nlp processing recognition tasks type