A Chinese automatic question answering technique based on semantic similarity

2021 ◽  
pp. 1-9
Author(s):  
Weijie Yang ◽  
Hong Ma
Keyword(s):  
Semantic Similarity ◽  
Experimental Verification ◽  
Question Answering ◽  
Short Term Memory ◽  
Open Domain ◽  
Short Term ◽  
Term Memory ◽  
Questions And Answers ◽  
Memory Network ◽  
Long Short Term Memory

In this paper, for the Chinese automatic question answering technology in open domain, in addition to considering the traditional association between questions and questions, the correlation between questions and answers is added. The cosine similarity between questions and answers is used as the semantic similarity between them. A bi-directional long short-term memory network (BiLSTM) is added between the question and question, answer and the answer to seek the association between the contexts. and an attention mechanism is added to make question and answer related. Finally, the experimental verification shows that the accuracy of automatic question answering by the proposed method reaches 70%.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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