Feature Analysis and Automatic Word Segmentation of Natural Language Processing Using Deep Learning

Natural Language Processing (NLP) based automatic paper evaluation using ensemble deep learning algorithms involves leveraging advanced machine learning techniques to assess and evaluate academic papers automatically. Ensemble techniques combine the predictions of various kinds of deep learning models to improve overall performance and robustness. The proposed method includes feature extraction, segmentation and classification process. The feature extraction is done using multi modal neural network. This helps the features to convert into same modal features from different modes. Then word segmentation process is carried using Recurrent Neural Network of Long Short Term Memory with Conditional Random Field (RNN-LSTM-CRF) and Gated Recurrent Unit with Conditional Random Field (RNN-GRU-CRF). Simulation results prove the RNN-GRUCRF provides better accuracy of word processing compared to the existing models.