Two-Stage Semantic Matching for Cross-Media Retrieval

Xu, Gongwen

doi:10.23940/ijpe.18.04.p21.795804

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Configuration e-commerce data description text features compared with ordinary text, IED configuration data description text usually involves e-commerce proper nouns [ 14 ]. Misdivision of words is easy to occur in the word segmentation stage, which leads to misclustering of word vectors by the language model [ 15 ]. Therefore, the article is in the classification package.…”

Section: E-commerce Text Mining Under Big Datamentioning

confidence: 99%

Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

Yuan

Tian

2023

Heliyon

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Section: E-commerce Text Mining Under Big Datamentioning

confidence: 99%

Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

Yuan

Tian

2023

Heliyon

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“…Use cost value to represent the difference between two items, and is the deviation item. By iteratively changing the word vectors of all words, the cost value is the smallest in the entire corpus, that is, the optimal word vector of all words in the corpus is obtained so that the word vector of the word is calculated through the context information [31]. The dataset contains a large amount of English text, and the word vector obtained by pretraining contains more accurate context information.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Semantic Matching Efficiency of Supply and Demand Text on Cross‐Border E‐Commerce Online Technology Trading Platforms

Chen

2021

Wireless Communications and Mobile Computing

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With the innovation of global trade business models, more and more foreign trade companies are transforming and developing in the direction of cross-border e-commerce. However, due to the limitation of platform language processing and analysis technology, foreign trade companies encounter many bottlenecks in the process of transformation and upgrading. From the perspective of the semantic matching efficiency of e-commerce platforms, this paper improves the logical and technical problems of cross-border e-commerce in the operation process and uses semantic matching efficiency as the research object to conduct experiments on the QQP dataset. We propose a graph network text semantic analysis model TextSGN based on semantic dependency analysis for the problem that the existing text semantic matching method does not consider the semantic dependency information between words in the text and requires a large amount of training data. The model first analyzes the semantic dependence of the text and performs word embedding and one-hot encoding on the nodes (single words) and edges (dependencies) in the semantic dependence graph. On this basis, in order to quickly mine semantic dependencies, an SGN network block is proposed. The network block defines the way of information transmission from the structural level to update the nodes and edges in the graph, thereby quickly mining semantics dependent information allows the network to converge faster, train classification models on multiple public datasets, and perform classification tests. The experimental results show that the accuracy rate of TextSGN model in short text classification reaches 95.2%, which is 3.6% higher than the suboptimal classification method; the accuracy rate is 86.16%, the F 1 value is 88.77%, and the result is better than other methods.

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“…With the developing of compressed sensing, sparse representation represents a sample (a test sample) e.g. an image or a text using an overcomplete dictionary (the training samples), and the representation is linear and naturally sparse [23][24][25][26]. The total training set is defined as the overcomplete dictionary A of k classes:…”

Section: Sparse Representation Classifiermentioning

confidence: 99%

Cross-Media Semantic Matching based on Sparse Representation

Zhai²,

Wang

et al. 2019

Teh. vjesn.

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With the rapid growth of multi-modal data, cross-media retrieval has aroused many research interests. In this paper, the cross-media retrieval includes two tasks: query image retrieves relevant text and query text retrieves relevant images. With the development of sparse representation, two independent sparse representation classifiers are used to map the heterogeneous features of images and texts into their common semantic space before implementing similarity comparison. The proposed method makes full use of semantic information, and it is effective in the retrieving task. The performance of this method was evaluated on Wiki dataset, NUS-WIDE dataset, Wiki dataset with CNN features and Pascal dataset with CNN features. The experimental results validate its effectiveness compared with several state-of-the-art algorithms on the Mean Average Precision and other performance indexes.

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Two-Stage Semantic Matching for Cross-Media Retrieval

Cited by 3 publications

References 25 publications

Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

Semantic Matching Efficiency of Supply and Demand Text on Cross‐Border E‐Commerce Online Technology Trading Platforms

Cross-Media Semantic Matching based on Sparse Representation

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