Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.
AMIA Annu Symp Proc
; 2022: 662-671, 2022.
Article
in En
| MEDLINE
| ID: mdl-37128396
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Semantics
/
Natural Language Processing
/
Unified Medical Language System
Limits:
Humans
Language:
En
Journal:
AMIA Annu Symp Proc
Year:
2022
Document type:
Article