Chinese causal event extraction using causality‐associated graph neural network

2021 ◽  
Author(s):  
Jianqi Gao ◽  
Xiangfeng Luo ◽  
Hao Wang
Keyword(s):  
Neural Network ◽  
Event Extraction ◽  
Causal Event

Causal event extraction using causal event element-oriented neural network

2021 ◽  
Vol 24 (6) ◽  
pp. 621
Author(s):  
Kai Xu ◽  
Jianqi Gao ◽  
Xiangfeng Luo ◽  
Xue Chen ◽  
Peng Wang
Keyword(s):  
Neural Network ◽  
Event Extraction ◽  
Causal Event

Causal event extraction using causal event element-oriented neural network

2021 ◽  
Vol 24 (6) ◽  
pp. 621
Author(s):  
Kai Xu ◽  
Peng Wang ◽  
Xue Chen ◽  
Xiangfeng Luo ◽  
Jianqi Gao
Keyword(s):  
Neural Network ◽  
Event Extraction ◽  
Causal Event

scholarly journals Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

2018 ◽  
Vol 10 (10) ◽  
pp. 95 ◽  
Author(s):  
Yue Wu ◽  
Junyi Zhang
Keyword(s):  
Neural Network ◽  
Attention Mechanism ◽  
Semantic Feature ◽  
Word Embedding ◽  
Event Extraction ◽  
Semantic Features ◽  
Improve Performance

Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel architecture for combining word representations: character–word embedding based on attention and semantic features. By using an attention mechanism, our method is able to dynamically decide how much information to use from word or character level embedding. With the semantic feature, we can obtain some more information about a word from the sentence. We evaluate different methods on the CEC Corpus, and this method is found to improve performance.

scholarly journals Multi-Level Attention Based BLSTM Neural Network for Biomedical Event Extraction

2019 ◽  
Vol E102.D (9) ◽  
pp. 1842-1850 ◽  
Author(s):  
Xinyu HE ◽  
Lishuang LI ◽  
Xingchen SONG ◽  
Degen HUANG ◽  
Fuji REN
Keyword(s):  
Neural Network ◽  
Event Extraction ◽  
Biomedical Event Extraction ◽  
Multi Level

scholarly journals Sleep Apnea Identification using Ecg and Ppg Signals Involving Neural Network

2019 ◽  
Vol 8 (2) ◽  
pp. 3552-3557
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Event Extraction ◽  
Short Term ◽  
Poincaré Plot ◽  
Apnea Detection ◽  
Sleep Diagnosis ◽  
The Individual ◽  
Global And Local

Sleep apnea is one of the hypothetically severe sleep disorders that often stops and begins to breathe. The undiagnosed sleep apnea can be very serious, resulting in fast decreases in blood oxygen levels, during which developed insulin resistance and type 2 diabetes may increase. Several people do not know their condition, though. Typical for sleep diagnosis is an overnight polysomnography (PSG) in a dedicated sleep laboratory. Since these exams are expensive and beds are restricted due to the need for trained employees to evaluate the full. An automatic detection technique would allow faster diagnosis and more patients to be analyzed. Hence detection of sleep apnea is compulsory so that it could be treated. This study established an algorithm that signaled a short-term electrocardiographic event extraction (ECG) and combined neural network methodologies for automatic sleep apnea detection. This study provides users with visual experiences through visual parameters such as HRV measurements, Poincare plot, global and local return map. This enables the doctor evaluate whether or not the individual is suffering from sleep apnea.

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