DC pole | Wartość | Język |
dc.contributor.author | Korniichuk, Ruslan | - |
dc.contributor.author | Boryczka, Mariusz | - |
dc.date.accessioned | 2021-10-06T10:17:02Z | - |
dc.date.available | 2021-10-06T10:17:02Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | "Procedia Computer Science" Vol. 192 (2021), s. 3677-3685 | pl_PL |
dc.identifier.issn | 1877-0509 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/21729 | - |
dc.description.abstract | The purpose of this paper is to investigate whether it is possible to predict text readability with ensemble-based classifiers. In this article, the authors calculated and analyzed the readability indices. In the next stage, they defined additional features for each text and determined the relationships between readability and features. Among the various tasks of machine learning, they chose the classification problem. The authors calculated and compared the accuracy of different machine learning models. After building the models, they proceeded to the Random decision forests model interpretation step using the SHAP method. The authors show that machine learning models based on only three features are capable of predicting text readability. Long sentences and a low percentage of stop words can cause low readability. The machine learning model shown in this paper allows to classify texts according to readability with a model accuracy of 0.9. | pl_PL |
dc.language.iso | en | pl_PL |
dc.rights | Uznanie autorstwa-Użycie niekomercyjne-Bez utworów zależnych 3.0 Polska | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/pl/ | * |
dc.subject | Ensemble Methods | pl_PL |
dc.subject | Averaging Methods | pl_PL |
dc.subject | Boosting Methods | pl_PL |
dc.subject | Classification | pl_PL |
dc.subject | Explainable Prediction | pl_PL |
dc.subject | Readability Indices | pl_PL |
dc.title | Averaging and boosting methods in ensemble-based classifiers for text readability | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.identifier.doi | 10.1016/j.procs.2021.09.141 | - |
Pojawia się w kolekcji: | Artykuły (WNŚiT)
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