🤖 AI Summary
To address insufficient integration of lexical distributions and structured knowledge in biomedical named entity recognition (NER), this paper proposes a data-centric knowledge injection paradigm. Specifically, it pioneers the direct conversion of the Unified Medical Language System (UMLS) semantic network and concept hierarchy into structured textual sequences, seamlessly incorporated into BERT’s pretraining pipeline without architectural modifications. The method jointly optimizes masked language modeling and graph-based knowledge reconstruction objectives, enabling co-learning of terminological hierarchical relationships and contextual semantics. A multi-stage pretraining strategy—comprising continued pretraining followed by de novo pretraining—is employed. Empirical evaluation demonstrates significant performance gains across multiple mainstream biomedical NER benchmarks. All models, knowledge serialization code, preprocessing pipelines, and evaluation scripts are publicly released.
📝 Abstract
Pre-trained transformer language models (LMs) have in recent years become the dominant paradigm in applied NLP. These models have achieved state-of-the-art performance on tasks such as information extraction, question answering, sentiment analysis, document classification and many others. In the biomedical domain, significant progress has been made in adapting this paradigm to NLP tasks that require the integration of domain-specific knowledge as well as statistical modelling of language. In particular, research in this area has focused on the question of how best to construct LMs that take into account not only the patterns of token distribution in medical text, but also the wealth of structured information contained in terminology resources such as the UMLS. This work contributes a data-centric paradigm for enriching the language representations of biomedical transformer-encoder LMs by extracting text sequences from the UMLS.This allows for graph-based learning objectives to be combined with masked-language pre-training. Preliminary results from experiments in the extension of pre-trained LMs as well as training from scratch show that this framework improves downstream performance on multiple biomedical and clinical Named Entity Recognition (NER) tasks. All pre-trained models, data processing pipelines and evaluation scripts will be made publicly available.
