Abstract
The number of users the usage of internet is mounting day by day. Currently, researches on the information using the retrieval model neural networks have been actively progressed for the retrieval of information and the classification of documents. Various types of algorithms have been applied for identification and quantification of the words weights in documents. As information technologies accelerate, it is necessary to understand the exact meaning of documents through analyzing the words, using the advanced methods of technologies. In this paper, specific keywords were used by word2vec to identify naturally fused word frequencies, semantic relationships, and directional text-ranks. Therefore, the neural network is the advanced mechanism to verify the semantic relationship between words and texts in a particular document. Our approach uses the Word2vec to capture the semantic features between words in the selected text, and meanwhile naturally integrate the word frequency, semantic relation.
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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2017R1D1A1B03030033).
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Tanvir, A.M., Kim, Y., Chung, M. (2020). Design and Implementation of an Efficient Web Crawling Using Neural Network. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_20
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DOI: https://doi.org/10.1007/978-981-13-9341-9_20
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