Soft Computing based Duplicate Text Identification in Online Community Websites

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-7
Year of Publication : 2020
Authors : Basavesha D, Dr. Y S Nijagunarya
  10.14445/22315381/IJETT-V68I7P201S

MLA 

MLA Style: Basavesha D, Dr. Y S Nijagunarya.  "Soft Computing based Duplicate Text Identification in Online Community Websites" International Journal of Engineering Trends and Technology 68.7(2020):1-7. 

APA Style: Basavesha D, Dr. Y S Nijagunarya. Soft Computing based Duplicate Text Identification in Online Community Websites  International Journal of Engineering Trends and Technology, 68(7),1-7.

Abstract
As the number of social media websites and applications are increasing the amount and the speed of data generation is also increasing and in turn the chances of having duplicates in the data are also increasing. The presence of duplicates will reduce the quality of data and also deteriorates the accuracy of the final results. Therefore, identifying and removing the duplicates is very important and it is considered to be a necessary step in data preprocessing and data integration. In this paper we have made an extensive review on the state-of-the art literature in the field of duplicate text identification. The paper consists of a survey on the works related to duplicate data identification, duplicate text identification and duplicate record identification. We have discussed generalized step by step procedure for duplicate text identification that is followed by most of the researchers. We described about word embedding techniques, similarity estimation techniques, and different soft computing techniques such as neural networks, fuzzy logic, evolutionary algorithms, Bayesian networks and support vector machines. We summarized the state-of-the-art works in three categories like, duplicate question identification in quora and stack overflow, text identification in documents and record identification in small and large datasets. Finally we also discussed about the different metrics used to measure the performance of the model developed for duplicate identification.

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Keywords
Duplicate text, soft computing, neural network, fuzzy logic, bag-of-words.