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- W4386207890 abstract "With the global outbreak of the novel coronavirus, China has also been seriously affected, and news reports about the epidemic have flooded the internet. For some vulnerable groups and the general public, identifying the authenticity of the epidemic news content remains to be studied. This paper mainly addresses how to obtain effective knowledge of COVID-19 in the epidemic news content, and determines the optimal semantic model of retrieval and matching by means of multi-model fusion. Finally, the retrieval and matching system is constructed to obtain the best-matched news content in the form of fact questions. Based on the above scenarios, the main research contents of this paper include the construction of the COVID-19 fact knowledge map and research on multi-model fusion of fact question and answer retrieval and matching. In order to obtain the best fact matching more accurately, the basic Word2vec, BERT model, and unsupervised SimCSE two-tower model are selected in this paper. And the optimal semantic model is determined through the weighted fusion of the three models, as well as the construction of the fact retrieval and matching system of COVID-19 news content. Combined with the above research content, the main fact retrieval model of the system is constructed to obtain the sorted epidemic fact question-and-answer data as the main knowledge source. For the general COVID-19 fact issues, such as pneumonia symptoms, and treatment opinions, a fact retrieval and matching system based on the COVID-19 news content has been preliminarily realized." @default.
- W4386207890 created "2023-08-28" @default.
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- W4386207890 date "2023-07-05" @default.
- W4386207890 modified "2023-10-18" @default.
- W4386207890 title "A Fact-Searching and Matching System Based on News Content About COVID-19" @default.
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- W4386207890 doi "https://doi.org/10.1109/snpd-winter57765.2023.10224054" @default.
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