Text similarity matching method on basis of vector space model

Abstract

The invention discloses a text similarity matching method on the basis of a vector space model. The text similarity matching method includes extracting keywords of texts, clustering all the keywords and generating a keyword concept tree; and computing the similarity of the texts according to the created keyword concept tree of the keywords in the texts to be translated, and acquiring texts in a translation depository according to the similarity. The texts in the translation depository are matched with the texts to be translated. According to the technical scheme, the test similarity matching method has the advantages that relations among the texts can be relatively accurately reflected, so that the similarity of the texts can be sufficiently reflected.

Description

technical field [0001] The invention relates to a computer technology, in particular to a text similarity matching method based on a vector space model. Background technique [0002] Now some commonly used text retrieval models include text-based retrieval models and structure-based retrieval models. Text-based retrieval models include: vector space model, approximate model, probability model and statistical language retrieval model; structure-based text retrieval models include: internal structure retrieval model, external structure retrieval model. [0003] Text similarity, that is, the numerical measure of the similarity between two texts, take two texts D1 and D2, if (D1∩D2) / (D1∪D2) is closer to 1, the higher the similarity between the two texts, Vice versa. In text retrieval technology, similarity calculation is mainly used to measure the similarity between text objects, and it is a basic calculation in data mining and natural language processing. The key technology ...

AI Technical Summary

Problems solved by technology

[0005] Since the keywords may have synonyms and polysemy, the accuracy of the similarity calculation results obtained by the traditional vector space model method is not high, and the results are often unsatisfactory; the keyword weighting algorithm is only to find the text and The relationship between keywords cannot be horizontally linked to the relationship between keywords in different texts, which brings the following problems to text retrieval:

[0006] (1) Keywords cannot accurately express user needs

[0007] It is difficult for users to choose accurate keywords to search, because it involves semantic mapping between queries and concepts

The query keywords given by the user cannot reflect the user's intention well

[0008] (2) Keywords cannot reflect the content of the text

When the query keywords and text keywords form a conceptual hyponymy relationship, it is more difficult to retrieve

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