Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition

• URL: http://arxiv.org/abs/2503.05373v1
• Date: Fri, 07 Mar 2025 12:29:21 GMT
• Title: Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition
• Authors: Linh Le, Guido Zuccon, Gianluca Demartini, Genghong Zhao, Xia Zhang,
• Abstract summary: We exploit additional evidence by making use of domain-specific semantic type dependencies.<n>In some cases NER effectiveness can be significantly improved by making use of domain-specific semantic type dependencies.
• Score: 24.179910886684745
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations. In this paper, we exploit additional evidence by also making use of domain-specific semantic type dependencies. 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.

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