Knowledge enhanced LSTM for coreference resolution on biomedical texts.
Bioinformatics
; 37(17): 2699-2705, 2021 Sep 09.
Article
em En
| MEDLINE
| ID: mdl-33705524
ABSTRACT
MOTIVATION Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events' attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. RESULTS:
In this article, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. AVAILABILITY AND IMPLEMENTATION The source code will be made available at https//github.com/zxy951005/KB-CR upon publication. Data is avaliable at http//2011.bionlp-st.org/ and https//github.com/UCDenver-ccp/CRAFT/releases/tag/v3.1.3. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
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Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China