[Purpose/significance] In view of the problem that it's difficult for researchers to find valuable information from large amounts of scientific and technological information in the Web, this paper constructs a comprehensive calculation method for information value. It can calculate the information value of Web technology information and help researchers find Web technology information of information value quickly and accurately.[Method/process] Taking overall consideration of the external feature and textual semantic feature of the information, this paper used deep learning (pretrained language model) BERT to construct information value calculation model based on the textual semantic feature, used the predictive output of the deep learning model to complete the scoring, and combined the original calculation method of the external feature of the information to get the final information value score.[Result/conclusion] The experimental results show that the information value calculation model based on the textual semantic feature can rank the information to different levels according to their information value score, which makes up for the problem of poor star differentiation in the original calculation method only based on the external feature of the information. And the final comprehensive evaluation results show that the information value calculation model proposed in this paper can also meet the needs of researchers in the practical application.
Zhang Min
,
Liu Huan
,
Ding Liangping
,
Fan Qing
. Research on the Web Technology Information Value Calculation Method Based on Deep Learning[J]. Library and Information Service, 2021
, 65(23)
: 70
-78
.
DOI: 10.13266/j.issn.0252-3116.2021.23.008
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