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Dataset of stopwords extracted from Uzbek texts

Filtering stop words is an important task when processing text queries to search for information in large data sets. It enables a reduction of the search space without losing the semantic meaning. The stop words, which have only grammatical roles and not contributing to information content still add...

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Detalles Bibliográficos
Autores principales: Madatov, Khabibulla, Bekchanov, Shukurla, Vičič, Jernej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9194838/
https://www.ncbi.nlm.nih.gov/pubmed/35712366
http://dx.doi.org/10.1016/j.dib.2022.108351
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author Madatov, Khabibulla
Bekchanov, Shukurla
Vičič, Jernej
author_facet Madatov, Khabibulla
Bekchanov, Shukurla
Vičič, Jernej
author_sort Madatov, Khabibulla
collection PubMed
description Filtering stop words is an important task when processing text queries to search for information in large data sets. It enables a reduction of the search space without losing the semantic meaning. The stop words, which have only grammatical roles and not contributing to information content still add up to the complexity of the query. Existing mathematical models that are used to tackle this problem are not suitable for all families of natural languages [1]. For example, they do not cover families of languages to which Uzbek can be included. In the present work, the collocation method of this problem is o ered for families of languages that include the Uzbek language as well. This method concerns the so-called agglutinative languages, in which the task of recognizing stop words is much more difficult, since the stop words are “masked” in the text. In this work the unigram, the bigram and the collocation methods are applied to the “School corpus” that corresponds to the type of languages being studied.
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spelling pubmed-91948382022-06-15 Dataset of stopwords extracted from Uzbek texts Madatov, Khabibulla Bekchanov, Shukurla Vičič, Jernej Data Brief Data Article Filtering stop words is an important task when processing text queries to search for information in large data sets. It enables a reduction of the search space without losing the semantic meaning. The stop words, which have only grammatical roles and not contributing to information content still add up to the complexity of the query. Existing mathematical models that are used to tackle this problem are not suitable for all families of natural languages [1]. For example, they do not cover families of languages to which Uzbek can be included. In the present work, the collocation method of this problem is o ered for families of languages that include the Uzbek language as well. This method concerns the so-called agglutinative languages, in which the task of recognizing stop words is much more difficult, since the stop words are “masked” in the text. In this work the unigram, the bigram and the collocation methods are applied to the “School corpus” that corresponds to the type of languages being studied. Elsevier 2022-06-03 /pmc/articles/PMC9194838/ /pubmed/35712366 http://dx.doi.org/10.1016/j.dib.2022.108351 Text en © 2022 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Madatov, Khabibulla
Bekchanov, Shukurla
Vičič, Jernej
Dataset of stopwords extracted from Uzbek texts
title Dataset of stopwords extracted from Uzbek texts
title_full Dataset of stopwords extracted from Uzbek texts
title_fullStr Dataset of stopwords extracted from Uzbek texts
title_full_unstemmed Dataset of stopwords extracted from Uzbek texts
title_short Dataset of stopwords extracted from Uzbek texts
title_sort dataset of stopwords extracted from uzbek texts
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9194838/
https://www.ncbi.nlm.nih.gov/pubmed/35712366
http://dx.doi.org/10.1016/j.dib.2022.108351
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