Comparative Study of Topic Modeling and Word Embedding Approaches for Web Service Clustering

2021 ◽  
Author(s):  
Neha Agarwal ◽  
Geeta Sikka ◽  
Lalit Kumar Awasthi
Keyword(s):  
Comparative Study ◽  
Web Service ◽  
Topic Modeling ◽  
Word Embedding ◽  
Service Clustering

Incorporating LDA With Word Embedding for Web Service Clustering

2018 ◽  
Vol 15 (4) ◽  
pp. 29-44 ◽  
Author(s):  
Yi Zhao ◽  
Chong Wang ◽  
Jian Wang ◽  
Keqing He
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Word Embedding ◽  
The Internet ◽  
Word Embeddings ◽  
Training Process ◽  
Web Service Discovery ◽  
Processing Data ◽  
Clustering Approach ◽  
Service Clustering

With the rapid growth of web services on the internet, web service discovery has become a hot topic in services computing. Faced with the heterogeneous and unstructured service descriptions, many service clustering approaches have been proposed to promote web service discovery, and many other approaches leveraged auxiliary features to enhance the classical LDA model to achieve better clustering performance. However, these extended LDA approaches still have limitations in processing data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering. Especially, the semantically relevant words of service keywords by Word2vec were used to train the word embeddings and then incorporated into the LDA training process. Finally, experiments conducted on a real-world dataset published on ProgrammableWeb show that the authors' proposed approach can achieve better clustering performance than several classical approaches.

A Comparative Study on Sentiment Analysis Influencing Word Embedding Using SVM and KNN

2021 ◽  
pp. 199-211
Author(s):  
Bachchu Paul ◽  
Sanchita Guchhait ◽  
Tanushree Dey ◽  
Debashri Das Adhikary ◽  
Somnath Bera
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Word Embedding

Context Aware Post-filtering for Web Service Clustering

2014 ◽  
Author(s):  
Banage T. G. S. Kumara ◽  
Incheon Paik ◽  
Hiroki Ohashi ◽  
Wuhui Chen ◽  
Koswatte R. C. Koswatte
Keyword(s):  
Web Service ◽  
Context Aware ◽  
Service Clustering
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