ARTE: Automatic Readability tool for english
ARTE is a tool that will automatically calculate a variety of readability formulas for texts. The readability formulas include classic formulas like Flesch-Kincaid Grade Level, SMOG, and Dale-Chall along with new formulas like the Coh-Metrix L2 Readability Index (CML2RI) and the Crowdsourced Algorithm of Reading Comprehension (CAREC)
ARTE takes plain text files as input (it will process all plain text files in a particular folder) and produces a comma separated values (.csv) spreadsheet that is easily read by any spreadsheet software.
Please use the following citation when referencing the Automatic Readability Tool for English (ARTE) in your work:
Choi, J. S., & Crossley, S. A. ARTE: Automatic Readability Tool for English. NLP Tools for the Social Sciences. linguisticanalysistools.org
Version 1.0 (released 6-8-2020)
ARTE User Manual
ARTE Index Description Sheet
ARTE for Mac
ARTE for Windows (7, 8, 10; 64-bit java required)
ARTE takes plain text files as input (it will process all plain text files in a particular folder) and produces a comma separated values (.csv) spreadsheet that is easily read by any spreadsheet software.
Please use the following citation when referencing the Automatic Readability Tool for English (ARTE) in your work:
Choi, J. S., & Crossley, S. A. ARTE: Automatic Readability Tool for English. NLP Tools for the Social Sciences. linguisticanalysistools.org
Version 1.0 (released 6-8-2020)
ARTE User Manual
ARTE Index Description Sheet
ARTE for Mac
ARTE for Windows (7, 8, 10; 64-bit java required)
ARTE is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Studies that have used ARTE
(please email me if we have missed your paper!)
Crossley, S. A., Skalicky, S., & Dascalu, M. (2019). Moving beyond classic readability formulas: New methods and new models. Journal of Research in Reading, 42 (3-4), 541-561.
Crossley, S. A., Skalicky, S., & Dascalu, M. (2019). Moving beyond classic readability formulas: New methods and new models. Journal of Research in Reading, 42 (3-4), 541-561.