KrNER: A Novel Named Entity Recognition Method Based on Knowledge Enhancement and Remote Supervision

Jinhua Du, Hao Yin

Abstract: Difficulty in recognizing complex entities and scarcity of annotated corpus have become the two major difficulties constraining effective information extraction from electronic medical records. Recent research focuses on utilizing complex feature engineering or large-scale pre-trained models to indirectly reduce the degradation of entity recognition caused by these two difficulties. In an attempt to directly address the difficulties, we propose KrNER, a new healthcare-oriented approach to named entity recognition. It utilizes knowledge enhancement and remote supervision techniques to effectively deal with the problem of recognizing complex entities, while guaranteeing better recognition results even in corpus-scarce contexts. Experiments show that KrNER is very effective and significantly outperforms the state-of-the-art Chinese medical named entity recognition models.