关键词: 文本文献, 信任事实, 可信语义, 深度学习, 智能搜索

Abstract: Aiming at the inaccuracy and dissatisfaction of traditional text search results, a text search method based on trust semantics and deep learning is proposed in this paper. Firstly, in order to fully extract the trust worthiness of the text, the trust degree of the text is calculated by means of the trust facts in the text and the annotation of the human-machine interaction： crawling a large amount of text literatures as the learning and training data by a web crawler, at the same time, building a deep learning neural network model, and training this model with supervised learning for evaluating the trust degree of text, by using the semantic matrix of text as input and the trust degree of text as output. Finally, the neural network model is applied to text and literature search. Through the search experiments in the field of “Chinese politics and party building”, it is shown that the proposed method is superior to the traditional search in terms of average trust degree.

Key words: text and literature, trust fact, trust semantics, deep learning, intelligent search