Identification of Industrial Land Usage Based on Natural Language Processing and Machine Learning
In the process of combing industrial land information,it is first necessary to determine the base number of land plots,among which the land usage of the land where patter spots are located is key information.Due to the lack of the clear identification of the keywords of relevant information in some early paper information,a lot of man-power and time are only wasted to read these paper contents or scan the data in documents,and finally manual judgments and summaries are made.Now,based on natural language processing and machine learning,an improved naive Bayes model is constructed by introducing important word weights to identify the required land information and compare it with actual correct information.The results show that after constructing the dictionary through ma-chine learning,the use of natural language processing technology greatly improves the accuracy and efficiency of the key information recognition of industrial land.
Pattern spotLand usageNatural language processingMachine learning