Operation and maintenance project management method based on intelligent word segmentation and deep learning

Abstract

The invention discloses an operation and maintenance project management method based on intelligent word segmentation and deep learning. The method comprises the steps of performing intelligent word segmentation on a declaration text and a declaration guide text in a project declaration according to a domain corpus biLSTM-CRF model; constructing a project declaration form space vector and a project declaration guide space vector; calculating a similarity value between a project declaration form space vector and a project declaration guide space vector through a convolutional neural network enhanced by an attention mechanism; finally, judging whether the project declaration passes automatic review or not through the similarity value. The project declaration process can be optimized, the project declaration period can be shortened, meanwhile, the declaration labor cost can be reduced, and the production efficiency can be improved.

Description

Technical field [0001] The invention relates to an operation and maintenance project management method based on intelligent word segmentation and deep learning. Background technique [0002] After Hinton et al. proposed deep learning in 2006, artificial neural networks began to gain attention in the field of natural language processing. At present, some results have been achieved in the application of natural language processing in combination with deep learning models. [0003] Language model is the first natural language processing problem to be studied using neural networks. In 2003, Bengio et al. proposed a word vector (Word embedding) method, which can transform the word mapping into an independent vector space; further combined with a nonlinear neural network to propose an N-Gram model; inspired by this, Collobert et al. based on the word vector method and Multi-layer one-dimensional convolutional neural network (Convolutional neural network, CNN), realizes a SENNA (Semantic...

AI Technical Summary

Problems solved by technology

First: The BERT system builds vectors in units of words, which cannot make full use of the results of intelligent word segmentation; second: the BERT system is more effective for matching short sentences, but it is longer for project applications and contains more values Type (such as company output value, project budget, etc.), nominal type (such as company type, company industry, etc.) and other application scenarios, there is no way to directly deal with it

Images