Medical Knowledge Attention Enhanced Neural Model for Named Entity Recognition in Chinese EMR

2018 ◽  
pp. 376-385 ◽  
Author(s):  
Zhichang Zhang ◽  
Yu Zhang ◽  
Tong Zhou
Keyword(s):  
Named Entity Recognition ◽  
Medical Knowledge ◽  
Neural Model ◽  
Entity Recognition ◽  
Named Entity

scholarly journals Nested Named Entity Recognition via Second-best Sequence Learning and Decoding

2020 ◽  
Vol 8 ◽  
pp. 605-620 ◽  
Author(s):  
Takashi Shibuya ◽  
Eduard Hovy
Keyword(s):  
Random Field ◽  
Sequence Learning ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Neural Model ◽  
Entity Recognition ◽  
Named Entities ◽  
Second Best ◽  
Named Entity ◽  
Better Than

When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively.

scholarly journals A Neural N-Gram-Based Classifier for Chinese Clinical Named Entity Recognition

2021 ◽  
Vol 11 (18) ◽  
pp. 8682
Author(s):  
Ching-Sheng Lin ◽  
Jung-Sing Jwo ◽  
Cheng-Hsiung Lee
Keyword(s):  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Neural Model ◽  
Entity Recognition ◽  
Chinese Word Segmentation ◽  
Named Entities ◽  
Named Entity ◽  
Biomedical Systems ◽  
N Gram

Clinical Named Entity Recognition (CNER) focuses on locating named entities in electronic medical records (EMRs) and the obtained results play an important role in the development of intelligent biomedical systems. In addition to the research in alphabetic languages, the study of non-alphabetic languages has attracted considerable attention as well. In this paper, a neural model is proposed to address the extraction of entities from EMRs written in Chinese. To avoid erroneous noise being caused by the Chinese word segmentation, we employ the character embeddings as the only feature without extra resources. In our model, concatenated n-gram character embeddings are used to represent the context semantics. The self-attention mechanism is then applied to model long-range dependencies of embeddings. The concatenation of the new representations obtained by the attention module is taken as the input to bidirectional long short-term memory (BiLSTM), followed by a conditional random field (CRF) layer to extract entities. The empirical study is conducted on the CCKS-2017 Shared Task 2 dataset to evaluate our method and the experimental results show that our model outperforms other approaches.

scholarly journals A Boundary-aware Neural Model for Nested Named Entity Recognition

2019 ◽  
Author(s):  
Changmeng Zheng ◽  
Yi Cai ◽  
Jingyun Xu ◽  
Ho-fung Leung ◽  
Guandong Xu
Keyword(s):  
Named Entity Recognition ◽  
Neural Model ◽  
Entity Recognition ◽  
Named Entity

scholarly journals A neural model for text localization, transcription and named entity recognition in full pages

2020 ◽  
Vol 136 ◽  
pp. 219-227
Author(s):  
Manuel Carbonell ◽  
Alicia Fornés ◽  
Mauricio Villegas ◽  
Josep Lladós
Keyword(s):  
Named Entity Recognition ◽  
Neural Model ◽  
Entity Recognition ◽  
Text Localization ◽  
Named Entity
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