Work

Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition

Year: 2025

Type: conference-paper

Abstract: . We encode the relation between a span of tokens matching a Unified Medical Language System (UMLS) concept and other tokens in the sentence. We implement our method and compare against different named entity recognition (NER) architectures (i.e., BiLSTM-CRF and BiLSTM-GCN-CRF) using different pre-trained clinical embeddings (i.e., BERT, BioBERT, UMLSBert). Our experimental results on clinical datasets show that in some cases NER effectiveness can be significantly improved by making use of domain-specific semantic type dependencies. Our work is also the first study generating a matrix encoding to make use of more than three dependencies in one pass for the NER task.

Source: PubMed

DOI: 10.48550/arxiv.2503.05373

Authors: Le, Linh, Zuccon, Guido, Demartini, Gianluca, Zhao, Genghong, Zhang, Xia

Institutions: The University of Queensland, Neusoft (China)

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Key Research and Development Program of China