Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning

TL;DR

This paper proposes a multimodal deep learning approach combining attention mechanisms, Word2Vec, and CNNs to improve image feature evaluation and reduce preprocessing complexity in NLP tasks.

Contribution

It introduces a novel integration of attention-based image representation with word embeddings and CNNs to enhance feature robustness and evaluation accuracy.

Findings

Improved image feature evaluation robustness

Reduced feature preprocessing complexity

Effective integration of Word2Vec with CNNs

Abstract

This project intends to study the image representation based on attention mechanism and multimodal data. By adding multiple pattern layers to the attribute model, the semantic and hidden layers of image content are integrated. The word vector is quantified by the Word2Vec method and then evaluated by a word embedding convolutional neural network. The published experimental results of the two groups were tested. The experimental results show that this method can convert discrete features into continuous characters, thus reducing the complexity of feature preprocessing. Word2Vec and natural language processing technology are integrated to achieve the goal of direct evaluation of missing image features. The robustness of the image feature evaluation model is improved by using the excellent feature analysis characteristics of a convolutional neural network. This project intends to improve the existing image feature identification methods and eliminate the subjective influence in the evaluation process. The findings from the simulation indicate that the novel approach has developed is viable, effectively augmenting the features within the produced representations.