Matches in SemOpenAlex for { <https://semopenalex.org/work/W4377966881> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W4377966881 endingPage "53994" @default.
- W4377966881 startingPage "53978" @default.
- W4377966881 abstract "Readability is the measure of how easier a piece of text is. Readability assessment plays a crucial role in facilitating content writers and proofreaders to receive guidance about how easy or difficult a piece of text is. In literature, classical readability, lexical measures, and deep learning based model have been proposed to assess the text readability. However, readability assessment using machine and deep learning is a data-intensive task, which requires a reasonable-sized dataset for accurate assessment. While several datasets, readability indices (RI) and assessment models have been proposed for military agencies manuals, health documents, and early educational materials, studies related to the readability assessment of computer science literature are limited. To address this gap, we have contributed Computer science (CS) literature dataset <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>AGREE</b> , comprising 42,850 learning resources(LR). We assessed the readability of learning objects(LOs) pertaining to domains of Computer Science (CS), machine learning (ML), software engineering (SE), and natural language processing (NLP). LOs consists of research papers, lecture notes and Wikipedia content of topics list of learning repositories for CS, NLP, SE and ML in English Language. From the statistically significant sample of LOs two annotators manually annotated LO’s text difficulty and established gold standard. Text readability was computed using 14 readability Indices (RI) and 12 lexical measures (LM). RI were ensembled, and readability measures were used to train the model for readability assessment. The results indicate that the extra tree classifier performs well on the AGREE dataset, exhibiting high accuracy, F1 score, and efficiency. We observed that there is no consensus among readability measures for shorter texts, but as the length of the text increases, the accuracy improves. The AGREE and SELRD datasets, along with the associated readability measures, provide a novel contribution to the field. They can be used to train deep learning models for readability assessment, develop recommender systems, and assist in curriculum planning within the domain of Computer Science. In the future, we plan to scale AGREE by adding more LOs and adding multimedia LOs. In addition, we would explore the use of deep learning methods for improved readability assessment." @default.
- W4377966881 created "2023-05-25" @default.
- W4377966881 creator A5010923976 @default.
- W4377966881 creator A5015650045 @default.
- W4377966881 creator A5023201370 @default.
- W4377966881 date "2023-01-01" @default.
- W4377966881 modified "2023-09-25" @default.
- W4377966881 title "Comprehensive Readability Assessment of Scientific Learning Resources" @default.
- W4377966881 cites W1507711477 @default.
- W4377966881 cites W1967427125 @default.
- W4377966881 cites W1988765540 @default.
- W4377966881 cites W2019416425 @default.
- W4377966881 cites W2044257893 @default.
- W4377966881 cites W2048587526 @default.
- W4377966881 cites W2138521612 @default.
- W4377966881 cites W2139450036 @default.
- W4377966881 cites W2159739762 @default.
- W4377966881 cites W2171575620 @default.
- W4377966881 cites W2178628967 @default.
- W4377966881 cites W2279039621 @default.
- W4377966881 cites W2502580032 @default.
- W4377966881 cites W2532161561 @default.
- W4377966881 cites W2587022626 @default.
- W4377966881 cites W2626427149 @default.
- W4377966881 cites W2735743598 @default.
- W4377966881 cites W2805805409 @default.
- W4377966881 cites W2806183494 @default.
- W4377966881 cites W2888211956 @default.
- W4377966881 cites W2962960603 @default.
- W4377966881 cites W2963094366 @default.
- W4377966881 cites W2989815961 @default.
- W4377966881 cites W3004798288 @default.
- W4377966881 cites W3021545172 @default.
- W4377966881 cites W3049363314 @default.
- W4377966881 cites W3158435821 @default.
- W4377966881 cites W3179257218 @default.
- W4377966881 cites W3202780679 @default.
- W4377966881 cites W3209080096 @default.
- W4377966881 cites W3212250752 @default.
- W4377966881 cites W4221068952 @default.
- W4377966881 cites W4255528569 @default.
- W4377966881 doi "https://doi.org/10.1109/access.2023.3279360" @default.
- W4377966881 hasPublicationYear "2023" @default.
- W4377966881 type Work @default.
- W4377966881 citedByCount "0" @default.
- W4377966881 crossrefType "journal-article" @default.
- W4377966881 hasAuthorship W4377966881A5010923976 @default.
- W4377966881 hasAuthorship W4377966881A5015650045 @default.
- W4377966881 hasAuthorship W4377966881A5023201370 @default.
- W4377966881 hasBestOaLocation W43779668811 @default.
- W4377966881 hasConcept C108583219 @default.
- W4377966881 hasConcept C154945302 @default.
- W4377966881 hasConcept C199360897 @default.
- W4377966881 hasConcept C204321447 @default.
- W4377966881 hasConcept C23123220 @default.
- W4377966881 hasConcept C2778143727 @default.
- W4377966881 hasConcept C41008148 @default.
- W4377966881 hasConceptScore W4377966881C108583219 @default.
- W4377966881 hasConceptScore W4377966881C154945302 @default.
- W4377966881 hasConceptScore W4377966881C199360897 @default.
- W4377966881 hasConceptScore W4377966881C204321447 @default.
- W4377966881 hasConceptScore W4377966881C23123220 @default.
- W4377966881 hasConceptScore W4377966881C2778143727 @default.
- W4377966881 hasConceptScore W4377966881C41008148 @default.
- W4377966881 hasLocation W43779668811 @default.
- W4377966881 hasOpenAccess W4377966881 @default.
- W4377966881 hasPrimaryLocation W43779668811 @default.
- W4377966881 hasRelatedWork W2081830265 @default.
- W4377966881 hasRelatedWork W2731899572 @default.
- W4377966881 hasRelatedWork W2773616286 @default.
- W4377966881 hasRelatedWork W2780447063 @default.
- W4377966881 hasRelatedWork W2939353110 @default.
- W4377966881 hasRelatedWork W3009238340 @default.
- W4377966881 hasRelatedWork W3215138031 @default.
- W4377966881 hasRelatedWork W4238586611 @default.
- W4377966881 hasRelatedWork W4321369474 @default.
- W4377966881 hasRelatedWork W4360585206 @default.
- W4377966881 hasVolume "11" @default.
- W4377966881 isParatext "false" @default.
- W4377966881 isRetracted "false" @default.
- W4377966881 workType "article" @default.