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106,541 to 106,550 of 106,653 Results
Plain Text - 9.0 KB - MD5: ac77b2c83d5dd014ab99fa167709f52c
Description of columns and value sets in frames_chu.csv
Plain Text - 6.6 MB - MD5: eb6acf24f3f647f52b574c8539f06c40
Full data set for the study. For a further description, see file_description.txt
Plain Text - 16.5 KB - MD5: f648583fe77f427da5bfab0e0f791320
List of verbal classes in the PROIEL/TOROT treebanks, with all Greek lemmata assigned to each class.
Plain Text - 5.1 KB - MD5: 6e9922057a98063e1c017d34a44870eb
R script for analysis of the data set.
Plain Text - 24.2 KB - MD5: eb2fa00754f10047a33547591a711362
Data
All potential occurrences of enchainement (rightward consonant linking across a word boundary) observed in the semi-formal and free conversation, as well as (non) enchainement of the consonant in the various cases.
Plain Text - 19.4 KB - MD5: ace085fa16f145c276bc8d7e0ed8d4aa
Data
All potential occurrences of enchainement (rightward consonant linking across a word boundary) observed in the text, as well as (non) enchainement of the consonant in the various cases.
Plain Text - 45.8 KB - MD5: dc5023674453af8a37fcf40425add095
Data
All potential occurrences of liaison observed in the semi-formal and free conversation, information on (non) liaison and (non) enchainement in the various cases, as well as, when a liaison consonant is realised, the type and quality of the consonant produced.
Plain Text - 36.4 KB - MD5: aa52b644765629719accbfa2aec30dbc
Data
All potential occurrences of liaison observed in the text, information on (non) liaison and (non) enchainement in the various cases, as well as, when a liaison consonant is realised, the type and quality of the consonant produced.
Plain Text - 19.2 KB - MD5: d6b2e91b351a27929128d10599e1e468
Python script
From input french_de_grepped.txt, creates output french_de_collated.txt. Used in Step 3 as outlined in introduction to HTML/Rmd file.
Plain Text - 17.7 MB - MD5: e4edbf552b4c8d0c5b9aff493319c7c0
Processed corpus-data files
Output of python script that collates relevant lines from Google n-grams corpus. Second file in data pipeline. Output of Step 3 as outlined in introduction of HTML/Rmd file.
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