Leverage Label and Word Embedding for Semantic Sparse Web Service Discovery

Sun, Chengai; Lv, Liangqiu; Tian, Gang; Wang, Qibo; Xiao-ning, Zhang; Guo, Lanhui

doi:10.1155/2020/5670215

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…e author takes the construction of the consumer brand choice cognitive model as the main line, and the consumer's cognitive structure and cognitive process as the two branches, an in-depth investigation and analysis, was carried out [10].…”

Section: Literature Reviewmentioning

confidence: 99%

The Influence of Brand Visual Communication on Consumer Psychology Based on Deep Learning

Wang

2022

Mathematical Problems in Engineering

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In order to solve the problem that there are few methods of users’ consumption psychology, the author proposes a research on the influence of brand visual communication on consumer psychology based on deep learning. First, establish the mapping relationship between experience level-product features-aspect words and then use aspect word extraction technology, mining users’ attention to different experience levels from user comments and dividing users into three types: instinctive preference, behavioral preference, and reflective preference; finally, the deep learning-based aspect sentiment analysis technology is used to calculate the user’s preference for the product and further analyze the characteristics of different types of users. Experimental results show that based on the application analysis of more than 900,000 JD.com mobile phone review data, three types of consumer preference user groups were obtained, of which instinctive preference users accounted for 41.6%; it is higher than behavioral preference users (33.01%) and reflection preference users (25.39%), and the consumption characteristics of the three types of users are analyzed from the aspects of mobile phone brand and price. It is proved that the author’s user portrait method can better express the consumption preferences of different types of users.

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Section: Literature Reviewmentioning

confidence: 99%

The Influence of Brand Visual Communication on Consumer Psychology Based on Deep Learning

Wang

2022

Mathematical Problems in Engineering

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“…It built a language comprehension model based on a massive content corpus [42]. Such models have demonstrated exceptionally effective language understanding by demonstrating that persuasion brings about most NLP undertakings [43].This approach depends on the interaction of computer programs with a dynamically active environment like a multiplayer game [44].…”

Section: ) Semantic-based Methodsmentioning

confidence: 99%

A Comprehensive Review of Arabic Text Summarization

et al. 2022

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The explosion of online and offline data has changed how we gather, evaluate, and understand data. It is frequently difficult and time-consuming to comprehend large text documents and extract crucial information from them. Text summarization techniques address the mentioned problems by compressing long texts while retaining their essential contents. These techniques rely on the fast delivery of filtered, high-quality content to their users. Due to the massive amounts of data generated by technology and various sources, automated text summarization of large-scale data is challenging. There are three types of automatic text summarization techniques: extractive, abstractive, and hybrid. Regardless of these previous techniques, the generated summaries are a long way from the summarization produced by human experts. Although Arabic is a widely spoken language that is frequently used for content sharing on the web, Arabic text summarization of Arabic content is limited and still immature because of several problems, including the Arabic language's morphological structure, the variety of dialects, and the lack of adequate data sources. This paper reviews text summarization approaches and recent deep learning models for this approach. Additionally, it focuses on existing datasets for these approaches, which are also reviewed, along with their characteristics and limitations. The most often used metrics for summarization quality evaluation are ROUGE1, ROUGE2, ROUGE L, and Bleu. The challenges that are encountered during Arabic text summarizing methods and approaches and the solutions proposed in each approach are analyzed. Many Arabic text summarization methods have problems, such as the lack of golden tokens during testing, being out of vocabulary (OOV) words, repeating summary sentences, lack of standard systematic methodologies and architectures, and the complexity of the Arabic language. Finally, providing the required corpora, improving evaluation using semantic representations, the lack of using rouge metrics in abstractive text summarization, and using recent deep learning models to adopt them in Arabic summarization studies is an essential demand.

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“…Because of the too much useless information of WSDL documents, Agarwal et al used a probability model to filter useless information before clustering [14]. Sun et al added neural networks to service clustering [4]. In general, these methods have a common limitation, and they are not suitable for processing service documents described in natural language.…”

Section: Wsdl Service Description Documentmentioning

confidence: 99%

“…rough the rapid development of Internet technology [1], clustering web services has become an effective method to solving service discovery [2][3][4], service composition [5,6], and service recommendation [7]. Firstly, there are increasing enterprises and institutions that encapsulate software functions or data into web services and publish them to the network.…”

Section: Introductionmentioning

confidence: 99%

A Web Service Clustering Method Based on Semantic Similarity and Multidimensional Scaling Analysis

Shan

2021

Scientific Programming

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Clustering web services is an effective method to solving service computing problems. The key insight behind it is to extract the vectors based on the service description documents. However, the brevity of natural language service description documents typically complicates the vector construction process. To circumvent the difficulty, we propose a novel web service clustering method to vectorize documents based on the semantic similarity, which can be calculated via WordNet and multidimensional scaling (WMS) analysis. We utilize the dataset from the ProgrammableWeb to conduct extensive experiments and achieve prominent advances in precision, recall, and F-measure.

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Leverage Label and Word Embedding for Semantic Sparse Web Service Discovery

Cited by 10 publications

References 27 publications

The Influence of Brand Visual Communication on Consumer Psychology Based on Deep Learning

The Influence of Brand Visual Communication on Consumer Psychology Based on Deep Learning

A Comprehensive Review of Arabic Text Summarization

A Web Service Clustering Method Based on Semantic Similarity and Multidimensional Scaling Analysis

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