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Domain Term Recognition Method Fused with Contextual Information

A technology of contextual information and identification methods, applied in the field of ontology learning, can solve the problem of low efficiency of term extraction

Inactive Publication Date: 2020-06-19
BEIJING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to solve the problem of low extraction efficiency of specific domain terms in ontology learning, this invention proposes a domain term recognition method that integrates contextual information, integrates statistical and linguistic methods, and draws on traditional domain correlation and domain consistency ideas , combined with the log-likelihood ratio, describe the distribution of candidate terms in different fields from the perspective of the recurrence times of candidate term context information, then calculate the domain attribute values ​​of candidate terms, and finally extract domain terms

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  • Domain Term Recognition Method Fused with Contextual Information
  • Domain Term Recognition Method Fused with Contextual Information
  • Domain Term Recognition Method Fused with Contextual Information

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Embodiment Construction

[0016] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0017] (1) The calculation formula adopted in the present invention is introduced

[0018] Assuming contextual information X and Y, then, the formula for calculating word form similarity between X and Y is as follows:

[0019]

[0020] Among them, CommonWord(X,Y) represents the number of identical words in X and Y, and WordNum(X) and WordNum(Y) represent the number of words contained in X and Y respectively.

[0021] Then, the word order similarity calculation formula of X and Y is as follows:

[0022] WordOrderSim(X,Y)=1-Rev(X,Y) / MaxRev(X,Y) (2)

[0023] Among them, Rev(X,Y) and MaxRev(X,Y) respectively represent the reverse sequence number and the maximum reverse sequence number of the natural number sequence with the same number of words in X and Y.

[0024] Therefore, the calculation formula of contextual similarity is as fo...

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Abstract

In order to solve the problem of low extraction efficiency of specific domain terms in ontology learning, this invention proposes a domain term recognition method that integrates contextual information, integrates statistical and linguistic methods, and draws on traditional domain correlation and domain consistency ideas , combined with the log-likelihood ratio, describe the distribution of candidate terms in different fields from the perspective of the recurrence times of the contextual information of candidate terms, then calculate the domain attribute values ​​of candidate terms, and finally extract domain terms. The field term recognition method that integrates contextual information described in the present invention can obtain very good term extraction accuracy, which can not only be applied in term extraction in fields such as depression medicine, but also can be used as a candidate concept in concept extraction methods Generate tools to use.

Description

technical field [0001] The invention relates to term extraction in the field of ontology learning, in particular to term extraction in the field of depression medicine. [0002] technical background [0003] Ontology has shown excellent performance in solving problems such as knowledge representation, knowledge organization and knowledge sharing. Therefore, it is widely used in information technology, artificial intelligence, knowledge engineering, knowledge management, information retrieval and other fields, especially the emergence of Semantic Web. , which makes Ontology propose a new solution for Web information sharing and brings broad prospects for its development. Ontology, as a clear specification of conceptual model, is a relational model between concepts. As a description of concepts, terms can be used to represent instances of concepts, so ontology term extraction becomes the primary task of ontology construction, which is of great significance to ontology learning...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F40/284G06F40/247G06K9/62
CPCG06F40/289G06F40/247G06F18/22
Inventor 董广场陈建辉钟宁
Owner BEIJING UNIV OF TECH