Unlocking the potential of keyword extraction: The need for access to high-quality datasets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570658" target="_blank" >RIV/70883521:28140/23:63570658 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2076-3417/13/12/7228" target="_blank" >https://www.mdpi.com/2076-3417/13/12/7228</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app13127228" target="_blank" >10.3390/app13127228</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unlocking the potential of keyword extraction: The need for access to high-quality datasets

Popis výsledku v původním jazyce

Keyword extraction is a critical task that enables various applications, including text classification, sentiment analysis, and information retrieval. However, the lack of a suitable dataset for semantic analysis of keyword extraction remains a serious problem that hinders progress in this field. Although some datasets exist for this task, they may not be representative, diverse, or of high quality, leading to suboptimal performance, inaccurate results, and reduced efficiency. To address this issue, we conducted a study to identify a suitable dataset for keyword extraction based on three key factors: dataset structure, complexity, and quality. The structure of a dataset should contain real-time data that is easily accessible and readable. The complexity should also reflect the diversity of sentences and their distribution in real-world scenarios. Finally, the quality of the dataset is a crucial factor in selecting a suitable dataset for keyword extraction. The quality depends on its accuracy, consistency, and completeness. The dataset should be annotated with high-quality labels that accurately reflect the keywords in the text. It should also be complete, with enough examples to accurately evaluate the performance of keyword extraction algorithms. Consistency in annotations is also essential, ensuring that the dataset is reliable and useful for further research.

Název v anglickém jazyce

Unlocking the potential of keyword extraction: The need for access to high-quality datasets

Popis výsledku anglicky

Keyword extraction is a critical task that enables various applications, including text classification, sentiment analysis, and information retrieval. However, the lack of a suitable dataset for semantic analysis of keyword extraction remains a serious problem that hinders progress in this field. Although some datasets exist for this task, they may not be representative, diverse, or of high quality, leading to suboptimal performance, inaccurate results, and reduced efficiency. To address this issue, we conducted a study to identify a suitable dataset for keyword extraction based on three key factors: dataset structure, complexity, and quality. The structure of a dataset should contain real-time data that is easily accessible and readable. The complexity should also reflect the diversity of sentences and their distribution in real-world scenarios. Finally, the quality of the dataset is a crucial factor in selecting a suitable dataset for keyword extraction. The quality depends on its accuracy, consistency, and completeness. The dataset should be annotated with high-quality labels that accurately reflect the keywords in the text. It should also be complete, with enough examples to accurately evaluate the performance of keyword extraction algorithms. Consistency in annotations is also essential, ensuring that the dataset is reliable and useful for further research.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

APPLIED SCIENCES-BASEL

ISSN

2076-3417

e-ISSN

2076-3417

Svazek periodika

13

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

001014027400001

EID výsledku v databázi Scopus

2-s2.0-85164024037