Corpus quality evaluation system

By adopting neural symbol fusion technology and multi-task quality scoring module in the corpus quality assessment system, the problem of inefficiency caused by traditional methods relying on manual is solved, and corpus quality assessment with high accuracy, interpretability and adaptability is achieved.

CN119807689APending Publication Date: 2025-04-11SHANGHAI CITIC DIGITAL TECH CO LTD
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Patent Information

Application Number
CN202411882581.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-19
Publication Date
2025-04-11

AI Technical Summary

Technical Problem

Traditional corpus quality evaluation methods rely too much on manual labor, resulting in inefficiency and difficulty in meeting the current large-scale corpus processing needs.

Method used

Adaptive multidimensional corpus quality evaluation system based on artificial intelligence is adopted, including feature extraction module, knowledge graph construction module, multi-head self-attention fusion module, logic reasoning module and multi-task quality scoring module, to achieve deep semantic understanding and structural analysis through the fusion of neural networks and symbolic logic.

Benefits of technology

It significantly improves the accuracy, comprehensiveness and interpretability of corpus quality evaluation, has strong adaptability, can adapt to various complex corpus evaluation scenarios, reduces manual participation, and improves evaluation efficiency.

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Abstract

The embodiment of the invention relates to the technical field of computers, and discloses a corpus quality evaluation system. The system comprises a feature extraction module used for determining an explicit rule feature and an implicit depth feature of a neural symbol according to an input corpus; the knowledge graph construction module is used for constructing a knowledge graph of the corpus according to the explicit rule features and the implicit depth features; the multi-head self-attention fusion module is used for determining fusion features according to the knowledge graph; the logical reasoning module is used for performing rule reasoning according to the fusion features to obtain a reasoning result; and the multi-task quality scoring module is used for generating a multi-dimensional quality score according to the reasoning result. The technical problem that a traditional method is difficult to meet the current efficiency requirement can be solved at least.
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