Petroleum Engineering Data Text Classification Using Convolutional Neural Network Based Classifier
In this paper, authors introduce a convolutional neural network (CNN) based classification approach for petroleum engineering event. Recently, text classification problem has raised substantial attention in the field of machine learning. The aim of text classification process is to assign correct label to a text on the basis of its content. However, due to the imprecise of feature representation, most of former text classification methods cannot export satisfying result. To solve this problem, authors propose a model based on convolutional neural network. Authors conduct experiments with real world petroleum engineering data text, artificially labelled into 6 categories. Beside purposed CNN based model, the authors also implement other baseline machine learning approaches: k nearest neighbor, random forest, support vector machines, recurrent neural network, gated recurrent units, long short term memory. Among all these models, our CNN based model reach best performance in text classification task. In the last part of this paper, authors compare and analysis their purpose and baseline model.
ICMLT '18