Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF.
BMC Med Inform Decis Mak
; 19(Suppl 3): 74, 2019 04 04.
Article
em En
| MEDLINE
| ID: mdl-30943972
ABSTRACT
BACKGROUND:
Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a few researchers have began to study entity recognition in Chinese clinical text.METHODS:
In this paper, a novel deep neural network, called attention-based CNN-LSTM-CRF, is proposed to recognize entities in Chinese clinical text. Attention-based CNN-LSTM-CRF is an extension of LSTM-CRF by introducing a CNN (convolutional neural network) layer after the input layer to capture local context information of words of interest and an attention layer before the CRF layer to select relevant words in the same sentence.RESULTS:
In order to evaluate the proposed method, we compare it with other two currently popular methods, CRF (conditional random field) and LSTM-CRF, on two benchmark datasets. One of the datasets is publically available and only contains contiguous clinical entities, and the other one is constructed by us and contains contiguous and discontiguous clinical entities. Experimental results show that attention-based CNN-LSTM-CRF outperforms CRF and LSTM-CRF.CONCLUSIONS:
CNN and attention mechanism are individually beneficial to LSTM-CRF-based Chinese clinical entity recognition system, no matter whether contiguous clinical entities are considered. The conribution of attention mechanism is greater than CNN.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Linguagem Natural
/
Redes Neurais de Computação
/
Registros Eletrônicos de Saúde
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article