News video title extraction algorithm based on deep learning

Abstract

In order to better serve the needs of news business, researchers apply the information extraction technology of news headlines to the news field to assist decision-making. This article designs the shared convolutional layer and RPN network in the detection network respectively, and improves the depth of the shared convolutional layer, using VGGNet instead of ZFNet. A classification method incorporating semantic enhancement is designed for fine-grained topic category news classification. Combined with the idea of modular fusion mechanism, a semantic-enhanced classification model Multiple-Fusion is proposed. The Bert module replaces the traditional Word2Vec for semantic vector characterization, and introduces Bi-LSTM to adaptively extract context features, strengthens feature expression through the self-attention mechanism and adjusts network weights, and finally makes the model achieve accurate classification. This article designs a novel word-level data enhancement strategy for text data enhancement, which solves the problems of fewer training corpus samples and model overfitting. This article proposes a video target extraction method based on deep learning. The algorithm framework includes particle filtering, pre-training features, convolutional neural networks, discriminative classifiers, and online parameter updates. This method deeply combines deep learning models and traditional target extraction methods and frameworks. The experimental results show that the method in this article has relatively outstanding performance, and can adapt to many interferences encountered in the extraction process and the change of the target itself, and has strong robustness.