Business logic code-free development method based on natural language understanding and conversion
A technology for natural language understanding and code development, applied in the field of codeless development of business logic based on natural language understanding and transformation, it can solve problems such as less research on system function modules, reduce development thresholds, improve development efficiency, and reduce software development. and maintenance costs
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Embodiment 1
[0088] Example 1, a code-free development method for business logic based on natural language understanding and conversion:
[0089] The first aspect is the development semantic extraction driven by natural language understanding. The core of development semantic extraction driven by natural language understanding is to extract key development elements for the next step of component matching. The invention treats the automatic extraction of key development elements in the natural language description as a sequence labeling problem. Through supervised training on massive natural language descriptions with annotations and code-free development requirements corpus, an efficient, high-accuracy, and high-generalization development semantic extraction model can be obtained. The model uses the BERT (Bidirectional Encoder Representation from Transformers, BERT) method to map the high-dimensional One-Hot Encoding (One-Hot Encoding) vector representing a word in a high-dimensional spac...
Embodiment 2
[0100] Example 2, an experiment of a code-free development method for business logic based on natural language understanding and transformation:
[0101] The code generation process is as follows figure 1 shown. Below, the implementation process of the method of the present invention is divided into four parts and explained in detail.
[0102] (1) Development Semantic Extraction Driven by Natural Language Understanding
[0103] In the present invention, the text input by the user has problems such as sparse text features, a small number of words but a large amount of information, complex semantics, and complex context. To solve this problem, the present invention adopts an intention recognition model of a bidirectional long-short-term memory network combined with an attention mechanism for semantic recognition.
[0104] Such as figure 2 As shown, suppose the input sequence is X=[x 1 , x 2 ,...,x n ], input X into the BiLSTM layer, and the hidden states before and after...
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