| dc.contributor.author | Nishanth, M | |
| dc.date.accessioned | 2022-03-08T09:02:33Z | |
| dc.date.available | 2022-03-08T09:02:33Z | |
| dc.date.issued | 2021 | |
| dc.identifier.citation | "Nishanth, M (2021) Sentence reading difficulty level analyze using NLP and CEFR framework. BSc. Dissertation Informatics Institute of Technology" | en_US |
| dc.identifier.issn | 2016327 | |
| dc.identifier.uri | http://dlib.iit.ac.lk/xmlui/handle/123456789/897 | |
| dc.description.abstract | " When reading to compare within any language Vocabulary is most of the part will affect for the readers. They need to faced Reading difficulty when they are reading a sentence or a paragraph in the text note. The vocabulary will depend on how many words the reader will understand with the exact meaning. This document will propose the solution for assessing the Reading Difficulty level of the text notes. It will compare with the reader's vocabulary level. It helps them to identify the difficult and easy sentence or paragraph in the uploaded text notes and the documents. There are several readability formulas are having to measure the readability of the text note. For my project, I used the most accurate method in calculating the readability by Flesh-Kincaid readability to measure the Readability level. It includes the Number of Words, No of Sentences, and Syllables per sentence as parameters. Overall, this entire study will suggest the reading difficulty level for each text note for enhanced their reading skills." | en_US |
| dc.language.iso | en | en_US |
| dc.subject | JavaScript | en_US |
| dc.subject | Python | en_US |
| dc.subject | Vocabulary Level | en_US |
| dc.subject | CEFR Framework | en_US |
| dc.subject | Reading Difficulty Level | en_US |
| dc.subject | Words - Natural Language Process | en_US |
| dc.title | Sentence reading difficulty level analyze using NLP and CEFR framework | en_US |
| dc.type | Thesis | en_US |