Differentiation extraction method and device, electronic equipment and computer readable storage medium
An extraction method and extraction device technology, applied in the field of financial technology, can solve the problems of high inspection difficulty, low inspection value, and difficulty of test case inspection, and achieve the effect of improving the speed of inspection
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no. 1 example
[0063] In an optional embodiment, the determination of the target test sample is of type M 1 Specifically, to determine the type of target test sample as a dictionary type S D (that is, the M determined at this time 1 For the dictionary type S D ), multiple S D Types of target test samples are vectorized to obtain the characterization vector P D , P D Each dimension in represents a dictionary attribute, the value is 0 or 1, 0 means this attribute has not been modified, 1 means this attribute has been modified, P D The vectors are as follows:
[0064] P D =(d1,d2,d3...di)
[0065] further endowed with S D The feature vector P of the dictionary-type target test sample D The weights are as follows:
[0066] W D =(W d1 ,W d2 ,W d3 …W di )
[0067] The above di represents whether the target test sample of the i-th dictionary is modified, W di Represents the weight value of the i-th dictionary attribute (that is, a in the content of the invention i ), and the follo...
no. 2 example
[0073] Similarly, in an optional embodiment, in the optional embodiment, it is determined that the target test sample is of type M 1 Specifically, to determine the target test sample type as the foreign key text type target test sample S F (that is, the M determined at this time 1 is the foreign key text type S F ), multiple S F Types of target test samples are vectorized to obtain the characterization vector P F , P F Each dimension represents a foreign key text attribute value, and the value is a value in the interval [0,1] to represent the difference degree of each dimension. The difference degree of each dimension is obtained by calculating the difference degree of the object pointed to by the attribute, and the attribute points to the object The calculation method of the difference degree is the same as that of the test case difference degree, and the vector is as follows:
[0074] P F =(f1,f2,f3...fi)
[0075] The weight vector of the following form is assigned to...
no. 3 example
[0083] Similarly, in an optional embodiment, in the optional embodiment, it is determined that the target test sample is of type M 1 Specifically, to determine the type of target test sample as the long text target test sample S L (that is, the M determined at this time 1 For long text type S L ), multiple S L Types of target test samples are vectorized to obtain the characterization vector P L , P L Each dimension represents a long text attribute, the value is [0,1] interval value, representing the degree of difference of each dimension, the degree of difference of each dimension can be calculated by TF / IDF, BM25 and other algorithms, the vector is as follows:
[0084] P L =(l1,l2,l3...ln)
[0085] Assign weight vectors of the following form to multiple long-text target test sample feature vectors:
[0086] W L =(W l1 ,W l2 ,W l3 …W li )
[0087] The above li represents the i-th long text attribute difference degree, W li Represents the weight value of the i-th ...
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