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CNN-based sky talk semantic consistency checking method

A technology of ground-to-air communication and verification method, which is applied in semantic analysis, natural language data processing, special data processing applications, etc., can solve the problem of undiscovered semantic consistency of convolutional neural network, etc., and achieve the effect of reducing the running time

Inactive Publication Date: 2016-12-14
CIVIL AVIATION UNIV OF CHINA
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AI Technical Summary

Problems solved by technology

However, there is currently no relevant method for the convolutional neural network model to be used for semantic consistency in land and air calls.

Method used

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  • CNN-based sky talk semantic consistency checking method
  • CNN-based sky talk semantic consistency checking method
  • CNN-based sky talk semantic consistency checking method

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Embodiment Construction

[0026] The CNN-based semantic consistency verification method for ground-to-air calls provided by the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] Such as figure 1 As shown, the CNN-based ground-air call semantic consistency check method provided by the present invention includes the following steps carried out in order:

[0028] 1) From the real land-to-air call recordings, select different repeating sentence pairs with good quality and in line with land-air call standards. Each repeating sentence pair is composed of two repeating sentences, and then stored in text txt format. These Repeat sentence pairs to form positive sample data with consistent semantics; at the same time, according to experience and the form of positive samples, professionals artificially form negative sample data with inconsistent semantics, and the corpus is composed of positive sample data and negative sample data;...

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Abstract

A CNN-based sky talk semantic consistency checking method comprises the steps of picking different repeat statement pairs from a real sky talk record to form positive sample data characterized in semantic consistency; forming negative sample data characterized in semantic inconsistency, and forming a corpus by means of the positive sample data and the negative sample data; conducting word segment on each repeat statement pair in the corpus; converting each word into a word vector, and forming a word vector library by means of the word vector; forming a corresponding matrix for each word; forming two semantic vectors of the repeat statement pairs with the matrix of each repeat statement as the input of a convolution nerve network input layer; calculating cosine similarity of two semantic vectors of the repeat statement pairs; conducting classification by means of the k-nearest neighbor method. By means of the parameter sharing advantage of the convolution nerve network, operation time can be shortened, and overall processing of statements can be conducted when the semantic vectors of statements are calculated.

Description

technical field [0001] The invention belongs to the technical field of semantic consistency verification of land and air calls in civil aviation transportation, and in particular relates to a method for verifying semantic consistency of land and air calls based on CNN. Background technique [0002] During the flight of an aircraft, in order to ensure the safe and efficient operation of the aircraft, air traffic controllers (referred to as "air traffic controllers") and pilots must be able to accurately and timely understand the intentions of both parties, so as to ensure that navigation instructions can be accurately communicated. The pilot needs to repeat the instructions of the air traffic controller to ensure the correctness of the instructions. This process is called readback. Correct readback is one of the important means to ensure flight safety. Therefore, improving the accuracy of pilot readback can effectively reduce the occurrence of flight accidents and incidents....

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G06N3/04
CPCG06F40/242G06F40/284G06F40/30G06N3/04
Inventor 杨金锋卢薇冰师一华贾桂敏
Owner CIVIL AVIATION UNIV OF CHINA
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