A modified algorithm of the latent semantic analysis for text processing in the Russian language

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441753" target="_blank" >RIV/00216208:11320/21:10441753 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1088/1742-6596/1715/1/012009" target="_blank" >https://doi.org/10.1088/1742-6596/1715/1/012009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/1715/1/012009" target="_blank" >10.1088/1742-6596/1715/1/012009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A modified algorithm of the latent semantic analysis for text processing in the Russian language

Popis výsledku v původním jazyce

The paper presents a methodology for analyzing texts in the Russian language. The methodology is based on the Latent Semantic Analysis (LSA) algorithm. A number of disadvantages of the classical method are considered, and modification methods of extracting N-grams from the text are proposed. The modified method allows one to reduce a number of extracted N-grams and an increasing the meaningfulness of the retrieved collection in comparison with a standard method. The reduction of the collection size leads to a reduced dimension of the TF-IDF matrix and accelerated the execution of the SVD method. The advantages of the developed machine learning algorithm are demonstrated on simple sentences. Owing to discussed ideas it becomes possible to effectively parallelize the text processing at the lemmatization step.

Název v anglickém jazyce

A modified algorithm of the latent semantic analysis for text processing in the Russian language

Popis výsledku anglicky

The paper presents a methodology for analyzing texts in the Russian language. The methodology is based on the Latent Semantic Analysis (LSA) algorithm. A number of disadvantages of the classical method are considered, and modification methods of extracting N-grams from the text are proposed. The modified method allows one to reduce a number of extracted N-grams and an increasing the meaningfulness of the retrieved collection in comparison with a standard method. The reduction of the collection size leads to a reduced dimension of the TF-IDF matrix and accelerated the execution of the SVD method. The advantages of the developed machine learning algorithm are demonstrated on simple sentences. Owing to discussed ideas it becomes possible to effectively parallelize the text processing at the lemmatization step.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Journal of Physics: Conference Series

ISBN

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ISSN

1742-6588

e-ISSN

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Počet stran výsledku

7

Strana od-do

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Název nakladatele

IOP Publishing Ltd

Místo vydání

Bristol

Místo konání akce

Akademgorodok

Datum konání akce

19. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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