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We isolated 2,936 rows from the dictionary that are monosyllables and converted their TRS to MTL. We only considered words from the first section of the dictionary because they appear to be frequently used, and ignored the second section. Then we counted the frequency of each MTL with Python's collections.Counter, which tells the number of dictionary rows matching each MTL, and got 1,813 unique MTL. Then we used Counter again on those results and found: | We isolated 2,936 rows from the dictionary that are monosyllables and converted their TRS to MTL. We only considered words from the first section of the dictionary because they appear to be frequently used, and ignored the second section. Then we counted the frequency of each MTL with Python's collections.Counter, which tells the number of dictionary rows matching each MTL, and got 1,813 unique MTL. Then we used Counter again on those results and found: | ||
* 1103 MTL (61%) and 1103 rows (38%) uniquely match one-to-one | * 1103 MTL (61%) and 1103 rows (38%) uniquely match one-to-one | ||
* as expected, | * as expected, most rows (1833 or 62%) have at least one homonym. Out of the corresponding 710 MTL (39%): | ||
** ''{{x|lie}}'', ''{{x|ky}}'', ''{{x|køf}}'' | ** ''{{x|lie}}'', ''{{x|ky}}'', and ''{{x|køf}}'' match the most (7 rows each), and ''{{x|cie}}'', ''{{x|kafn}}'', ''{{x|kefng}}'', ''{{x|sefng}}'', ''{{x|kaf}}'', ''{{x|kaq}}'', ''{{x|zngf}}'', ''{{x|ti}}'', ''{{x|sw}}'', ''{{x|leeng}}'', and ''{{x|kerng}}'' match 6 rows each | ||
** most of the homonyms cover two rows (443 MTL (24%), 886 rows (30%)) | |||
** | ** some MTL cover three rows (173 MTL (10%), 519 rows (18%)) | ||
** some MTL | ** a small fraction match up four to seven rows (94 MTL (5%), 428 rows (15%)) | ||
** a small | |||
[[File:rows and matching MTL vs. match level.png|thumb|none]] | [[File:rows and matching MTL vs. match level.png|thumb|none]] | ||
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