SocialNER2.0: A comprehensive dataset for enhancing named entity recognition in short human-produced text

Abstract

Named Entity Recognition (NER) is an essential task in Natural Language Processing (NLP), and deep learning-based models have shown outstanding performance. However, the effectiveness of deep learning models in NER relies heavily on the quality and quantity of labeled training datasets available. A novel and comprehensive training dataset called SocialNER2.0 is proposed to address this challenge. Based on selected datasets dedicated to different tasks related to NER, the SocialNER2.0 construction process involves data selection, extraction, enrichment, conversion, and balancing steps. The pre-trained BERT (Bidirectional Encoder Representations from Transformers) model is fine-tuned using the proposed dataset. Experimental results highlight the superior performance of the fine-tuned BERT in accurately identifying named entities, demonstrating the SocialNER2.0 dataset’s capacity to provide valuable training data for performing NER in human-produced texts.