Keywords extraction in Chinese–Vietnamese bilingual news based on hypergraph

Jiaxin Zhai, Shengxiang Gao, Zhengtao Yu, Zequan Fan, Li Liu, Hua Lai and Yafei Zhang

International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 11, 1550147718811107

Abstract: The keywords extraction of bilingual news events in China and Vietnam has a very important role in understanding bilingual news events. It can quickly locate and briefly compare the news of the same events reported by the two countries. Chinese–Vietnam news texts are typically unstructured big data. How to extract the keywords that characterize the news in these unstructured data is the difficult problem of unstructured big data analysis. Bilingual documents are difficult to understand because bilingual Chinese and Vietnamese are not in the same language space. However, the hypergraph of the hypergraph model can better express the multiple relations of the vocabulary association and the entity association for bilingual news. Therefore, a method based on hypergraph for bilingual news keywords extraction is proposed. In this method, bilingual news words are extracted to construct a bilingual word set, and the words are taken as vertices. Chinese–Vietnamese sentences and bilingual words with the same semantic meaning as different types of hyperedges and the bilingual word frequency are used as the attribute to construct a bilingual news item word hypergraph model. Then, the directional diffusion algorithm in the wireless sensor network is used to iteratively calculate the weights of the vertices so as to realize the extraction of keywords in the Chinese–Vietnam bilingual news. The experimental results show that the proposed hypergraph method is better than the single-document extraction method, which can better obtain the keywords of the bilingual unstructured text data.

Keywords: Chinese–Vietnamese; bilingual news events; keywords extraction; hypergraph model; directional diffusion (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:11:p:1550147718811107

DOI: 10.1177/1550147718811107

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