Enhance Both Text and Label: Combination Strategies for Improving the Generalization Ability of Medical Entity Extraction

Abstract

AbstractThis paper describes our approach for the Chinese medical Named Entity Recognition (NER) and event extraction tasks organized by the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2021. For the NER task, we need to identify the entity boundaries and category labels of six types of entities from Chinese electronic medical records (EMR). And for the Event Extraction task, we need to recognizes a type of tumor event from Chinese EMR, which contains three tumor-related attributes. This medical entity and event extraction task has two main challenges: 1) How to build a unified modeling framework for entity and event extraction. 2) How to improve the generalization ability of medical entity extraction. For these two challenges, we use a sequence labeling framework based on entity extraction to unify the above two tasks. Based on the pre-trained model, we propose a combined strategy of unsupervised text mode enhancement and label mode enhancement. In the end, it ranked second without any post-processing.KeywordsNamed Entity RecognitionEvent extractionElectronic medical records