Construction scheme category and text classification model realization research classification

Abstract

Construction scheme is one of the most important contents of construction organization design. The application of natural language processing to extract and review construction schemes from unstructured texts can improve review efficiency, enhance preparation quality, and identify potential safety and quality risks for early construction warnings. To extract construction schemes from unstructured texts, it is necessary to first clarify the content composition of different types of construction schemes and classify the contents of relevant paragraphs. Aiming at the classification of unstructured construction scheme paragraphs, classification of construction organization design paragraphs of urban pipe network engineering was taken as samples on the basis of in-depth study of construction scheme categories and content composition framework, and proposes a paragraph text classification model integrating Albert and TextRCNN. This model uses Albert pretraining language model for word embedding, and input the generated word vector into TextRCNN classifier to complete text classification. It outperforms Albert-TextCNN, which has the best classification effect among the other three models, and the accuracy increases by 0.79%. The experiment shows that: TextRCNN combined with Albert can effectively classify the contents of the construction organization design paragraphs, providing a basis for further construction scheme extraction.

Keywords

construction scheme / paragraph text / Albert / TextRCNN