Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-predicted outputs to entity categories, which are complicated undertakings. To address these challenges, we present ContrastNER, a prompt-based NER framework that employs both discrete and continuous tokens in prompts and uses a contrastive learning approach to learn the continuous prompts and forecast entity types. The experimental results demonstrate that ContrastNER obtains competitive performance to the state-of-the-art NER methods in high-resource settings and outperforms the state-of-the-art models in low-resource circumstances without requiring extensive manual prompt engineering and verbalizer design.
@article{arxiv.2305.17951,
title = {ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER},
author = {Amirhossein Layegh and Amir H. Payberah and Ahmet Soylu and Dumitru Roman and Mihhail Matskin},
journal= {arXiv preprint arXiv:2305.17951},
year = {2023}
}