On the data scarcity problem of neural-based named entity recognition - Zhou, Ran (Nanyang Technological University, 2024)
The data scarcity problem in neural-based Named Entity Recognition (NER) refers to the challenge of limited annotated data available for training NER models. Collecting and annotating large amounts of labeled data for various languages and domains can be time-consuming, expensive, and sometimes even impractical. This lack of labeled data can hinder the performance of neural-based NER models, as they require a substantial amount of annotated examples to learn effectively. With limited training data, neural-based NER models may struggle to generalize well and accurately identify unseen named entities in out-of-domain text or from a different language. They may be prone to overfitting, where the model becomes too specific to the training data and fails to generalize to new data, leading to reduced overall performance.