Studying search alphabet distortion impact on entities identification based on data frequency analysis

DOI: 10.34759/trd-2020-115-16

Аuthors

Malshakov G. V.

e-mail: malshakov@mail.ru

Abstract

The article recounts the results of studying the impact of the alphabet tokens degree of occurrence in the entity objects of the subject domain on the identification of entities based on data frequency analysis with various strategies for tokens exclusion.

The following dependences are shown:

– Dependence of autocorrelation on minimum allowable number of characters in tokens for various samplings (as a percentage of the initial data);

– Dependence of covering the entity objects alphabet of the subject domain on limitation on the number of characters in the alphabet tokens;

– Dependence of the number of correctly recognized entities of the subject domain for various samplings volumes on the limitation of the number of characters in lexemes of the alphabet of the subject domain entity’s frequency identification at unfavorable strategy for the subject domain entity objects exclusion;

– Dependence of the number of correctly recognized subject domain entities for various samplings sizes on the limitation on the number of characters in the lexemes of the alphabet of the frequency identification of the subject domain entity at a favorable strategy of excluding objects of the subject domain entities.

The impact of the degree of occurrence of alphabet lexemes in the entity objects of the subject domain and the strategy of excluding alphabet lexemes on the entities identification based on data frequency analysis was established.

The above said studies allow reducing the number of errors in identifying domain entities based on the data frequency analysis while ensuring the interoperability of the applied software.

Keywords:

error, identification, entity, application software, data frequency analysis, correlation

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