Semantic similarity-based text extraction data similarity matching method

Through the text extraction data similarity matching method based on semantic similarity, the document topic vector is extracted using BERT and LDA models, the problem of inaccurate text matching in the prior art is solved, and efficient matching between unstructured data and structured data is achieved.

CN119720991APending Publication Date: 2025-03-28BEIJING INST OF COMP TECH & APPL
View PDF 0 Cites 4 Cited by

Patent Information

Application Number
CN202411699658.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-26
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The prior art is difficult to achieve text matching accurately and efficiently, especially in the similarity matching between unstructured data and structured data.

Method used

A similarity matching method for text extraction data based on semantic similarity is proposed. By obtaining and preprocessing entity data, the initial semantic similarity model is trained, the document topic vector is extracted using BERT and LDA models, the similarity between document topics and event fields is calculated, and the semantic similarity matching between the text to be matched and the target text is achieved.

Benefits of technology

It realizes automatic matching of noun text and standard entity text in unstructured documents, improves the accuracy of document classification and the accuracy of converting unstructured data into structured data, and provides an effective operational preparation for semantic similarity matching.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN119720991A_ABST
    Figure CN119720991A_ABST
Patent Text Reader

Abstract

The invention relates to a text extraction data similarity matching method based on semantic similarity, and belongs to the field of natural language processing. According to the method, entity data is obtained and processed to serve as a standard word bank of the entity data, initial semantic similarity model training is conducted, candidate texts in documents are selected, document topics and distribution of the document topics are obtained, and the document categories are determined by calculating the topic similarity between the document topics and all event fields. Creating an event extraction template, extracting the document data to the corresponding event extraction template according to the document subject category, converting the unstructured data into structured data, identifying entity data from the structured data as a to-be-matched text, and determining the similarity between the to-be-matched text and the target text by using a semantic similarity model. According to the method and the device, automatic matching of the noun text and the standard entity text in the unstructured document is realized.
Need to check novelty before this filing date? Find Prior Art

Citation Information

Cited By

  • Multi-dimensional data matching training processing method based on multi-type database files

    CN120354147A

  • A multi-dimensional data matching training processing method based on multi-type database files

    CN120354147B

  • Text classification method based on document similarity

    CN122285904A

  • A text classification method based on document similarity

    CN122285904B