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Cross-language query post-translation consequent extension method based on complete weighted positive and negative mode

A fully weighted, extended method technology, applied in cross-language information retrieval query expansion, Internet information retrieval field

Inactive Publication Date: 2017-12-29
GUANGXI UNIVERSITY OF FINANCE AND ECONOMICS
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AI Technical Summary

Problems solved by technology

[0005] The present invention applies fully weighted positive and negative association pattern mining to post-translation extension of cross-language query, and proposes a post-translation extension method of cross-language query based on fully weighted positive and negative patterns, which is applied to the field of cross-language information retrieval and can solve the problem of The long-standing problems of query subject drift and word mismatch in cross-language information retrieval can improve the performance of cross-language information retrieval, and can also be applied to cross-language search engines to improve the retrieval performance of search engines such as recall rate and precision rate

Method used

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  • Cross-language query post-translation consequent extension method based on complete weighted positive and negative mode
  • Cross-language query post-translation consequent extension method based on complete weighted positive and negative mode
  • Cross-language query post-translation consequent extension method based on complete weighted positive and negative mode

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example

[0095] Example: If C k =(t 1 ∪t 2 ∪t 3 ∪t 4 ) (support degree is 0.65), its single item t 1 , t 2 , t 3 and t 4 The support degrees of are 0.82, 0.45, 0.76 and 0.75 respectively, and its 2_sub-itemset and 3_sub-itemset (t 1 ∪t 2 ), (t 1 ∪t 3 ), (t 1 ∪t 4 ), (t 2 ∪t 3 ), (t 2 ∪t 4 ), (t 1 ∪t 2 ∪t 3 ), (t 1 ∪t 2 ∪t 4 ), (t 2 ∪t 3 ∪t 4 ) support degrees are 0.64, 0.78, 0.75, 0.74, 0.67, 0., 66, 0.56, 0.43 respectively, then the single item with the largest support degree (value 0.82) is t 1 , the sub-itemset with the largest support (value 0.78) in its 2_subitemset and 3_subitemset is (t 1 ∪t 3 ), then use formula (14) to calculate the positive itemset (t 1 ∪t 2 ∪t 3 ∪t 4 ) is 0.81. Its calculation process is as follows:

[0096]

[0097] 4. Improvement degree of fully weighted association rules

[0098] The limitation of the traditional association rule evaluation framework (support-confidence) is that it ignores the itemset support in the ru...

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Abstract

The present invention provides a cross-language query post-translation consequent extension method based on complete weighted positive and negative mode. The method comprises: firstly, translating a source language query into a target language query and searching a target language document for the target language query, and extracting the preliminarily searched document and constructing a preliminarily searched related document set after related determination of a user; then, by adopting a positive and negative association mode mining technology based on a support-association-enhancement-confidence evaluation framework and oriented to cross-language query extension, mining the preliminarily searched related document set for a feature word positive and negative association rule mode containing a query word item, and constructing a feature word positive and negative association rule library; and extracting a complete weighted positive and negative association rule mode whose rule antecedent is the query word item from the library, and using a positive association rule consequent feature word as a candidate extension word and a negative association rule consequent feature word as a negative extension word, and removing the negative extension word from the candidate extension word to obtain a final consequent extension word for realizing cross-language query post-translation consequent extension. The method provided by the present invention can improve cross-language information retrieval performance and has higher application value and better market prospect.

Description

technical field [0001] The invention belongs to the field of Internet information retrieval, in particular to a cross-language query post-translation extension method based on a fully weighted positive and negative mode, which is suitable for fields such as cross-language information retrieval query expansion. Background technique [0002] Cross-Language Information Retrieval (CLIR) began to receive attention and attention in the late 1990s. Now we are in an era of multilingual networks. Language-diversified Internet resources have become big data information resources, and cross-language retrieval tools with good performance are urgently needed. Therefore, cross-language information retrieval has become an urgent research technology in the field of information retrieval. [0003] Cross-language information retrieval refers to the technology of retrieving information resources in other languages ​​in the form of a query in one language. The language used to express user quer...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F17/28G06F17/27
CPCG06F16/3337G06F16/951G06F40/289G06F40/58
Inventor 黄名选
Owner GUANGXI UNIVERSITY OF FINANCE AND ECONOMICS
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