Abstract
In this paper, we compare two models of document similarity search over a text stream of articles which are collected daily from online News sites. The first model uses the word to vector (Word2Vec), neural-network-based document embedding is known as the document to vector (Doc2Vec) and k-NN technique to perform similarity search in a tree structure called M-Tree. The second model applies Gensim model to do the same job of document similarity search. We use the metric which measures the accuracy of the documents similar to document d when considering if they are in the same category with the document d or not. We also do the experiment and evaluation, analyze experimental results, discuss and propose solutions for improvement. Our main contributions are to compare the two solutions in performing document similarity queries.
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Acknowledgments
This research is funded by Thu Dau Mot university, Binh Duong, and Vietnam National University Ho Chi Minh City (VNU-HCMC) under the grant number B2017-26-02.
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Hong, T.V.T., Do, P. (2020). Comparing Two Models of Document Similarity Search over a Text Stream of Articles from Online News Sites. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2019. Advances in Intelligent Systems and Computing, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-33585-4_38
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DOI: https://doi.org/10.1007/978-3-030-33585-4_38
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