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Natural Language Processing (NLP) is a field of study that develops software capable of interpreting human speech for mechanical use.Words are the building blocks of advanced grammatical and semantic analysis, and word segmentation is often the first order of business for natural language processing.This paper introduces the feature extraction method of deep learning and applies the ideas of deep learning to multi-modal feature extraction in order to address the practical problem of huge structural differences between different data modalities in a multi-modal environment.In this study, we present a neural network that can process information from several sources at once.Each mode is represented by a separate multilayer sub-neural network structure.It's purpose is to transform features from one mode to another.In order to solve the issues of current word segmentation techniques not being able to ensure long-term reliance on text semantics and lengthy training prediction time, a hybrid network English word segmentation processing approach is presented.This approach uses the BI-GRU (Bidirectional Gated Recurrent Unit) to segment English words and the CRF (Conditional Random Field) model to sequentially annotate sentences, which eliminates the long-distance dependency of text semantics and reduces the time needed to train the network and predict its performance.Compared to the BI-LSTM-CRF (Bidirectional-Long Short Term Memory-Conditional Random Field) model, the experimental results reveal that this technique achieves equivalent processing effects on word segmentation, while also boosting processing efficiency by a factor.
Devi et al. (Mon,) studied this question.