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| Natura: | Preprint |
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2023
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| Accesso online: | https://arxiv.org/abs/2301.02975 |
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| _version_ | 1866911962379583488 |
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| author | Lee, Bruce W. Lee, Jason Hyung-Jong |
| author_facet | Lee, Bruce W. Lee, Jason Hyung-Jong |
| contents | Traditional English readability formulas, or equations, were largely developed in the 20th century. Nonetheless, many researchers still rely on them for various NLP applications. This phenomenon is presumably due to the convenience and straightforwardness of readability formulas. In this work, we contribute to the NLP community by 1. introducing New English Readability Formula (NERF), 2. recalibrating the coefficients of old readability formulas (Flesch-Kincaid Grade Level, Fog Index, SMOG Index, Coleman-Liau Index, and Automated Readability Index), 3. evaluating the readability formulas, for use in text simplification studies and medical texts, and 4. developing a Python-based program for the wide application to various NLP projects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_02975 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Traditional Readability Formulas Compared for English Lee, Bruce W. Lee, Jason Hyung-Jong Computation and Language Machine Learning Traditional English readability formulas, or equations, were largely developed in the 20th century. Nonetheless, many researchers still rely on them for various NLP applications. This phenomenon is presumably due to the convenience and straightforwardness of readability formulas. In this work, we contribute to the NLP community by 1. introducing New English Readability Formula (NERF), 2. recalibrating the coefficients of old readability formulas (Flesch-Kincaid Grade Level, Fog Index, SMOG Index, Coleman-Liau Index, and Automated Readability Index), 3. evaluating the readability formulas, for use in text simplification studies and medical texts, and 4. developing a Python-based program for the wide application to various NLP projects. |
| title | Traditional Readability Formulas Compared for English |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2301.02975 |