Similarity calculation method based on text and semantics, server and storage medium

A technology of semantic similarity and text similarity, which is applied in text database query, unstructured text data retrieval, special data processing applications, etc., can solve the problems of diverse expressions in difficult conversations, single expression methods, etc., to ensure diverse expressions , Improve intelligence and reduce the effect of text information loss
CN110222154APending Publication Date: 2019-09-10武汉斗鱼鱼乐网络科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
武汉斗鱼鱼乐网络科技有限公司
Publication Date
2019-09-10

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Abstract

The embodiment of the invention discloses a similarity calculation method based on texts and semantics, a server and a storage medium, and belongs to the field of natural language processing. The method comprises the following steps of: selecting any two statement texts expressed in a vectorization manner; calculating a projection distance of two texts in a vector space to obtain semantic similarity of the two texts, calculating text similarity according to a proportion of a text superposition part of the two texts in a global text formed by two statement texts, respectively setting weights of the texts and the semantic similarity, and comprehensively calculating scores of the texts and the semantic similarity. According to the technical scheme, semantics and text similarity of the session text can be considered, diversified content expressions are provided for the computer session, and single and repeated replies are effectively avoided.
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Description

technical field

[0001] The invention relates to the field of natural language processing, in particular to a similarity calculation method based on text and semantics, a server and a storage medium. Background technique

[0002] When conducting a man-machine dialogue or a computer receiving natural language instructions, the computer must process and understand human language. Due to the diverse expressions of human language, it is inevitable to understand the literal meaning and semantics when performing natural language processing (NLP). diversity.

[0003] However, the similarity calculation of existing conversational content often only considers the understanding of semantics. After the text is segmented, the semantic similarity is calculated through frequency statistics or text clustering based on vocabulary or vocabulary sets, or text similarity is calculated based on cosine. Unilaterally considering the similarity of text literals or text semantics, it is difficult ...

Claims

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