09-12-2024, 11:50 AM
Hello Mauro,
If I understand correctly your "chunkification" process, the pattern matching is greedy: this is the first of the three possibilities in the example that I posted earlier on the first page of this thread, and I don't see why it is a better choice that the other two:
If I understand correctly your "chunkification" process, the pattern matching is greedy: this is the first of the three possibilities in the example that I posted earlier on the first page of this thread, and I don't see why it is a better choice that the other two:
(28-11-2024, 10:10 AM)nablator wrote: You are not allowed to view links. Register or Login to view.If some words are indeed "separable" (as suggested by M. Zattera's article), maximizing the F1 score or any metric is futile: the model is not meant to match "separable" words. Fitting the model to data containing "separable" words is probably what creates the wrap-around effect in the MZ slot sequence: slots 7-11 have 6 glyphs in common with slots 0-2, resulting in multiple possibilities of separation: for example sheolkeedy (in f115v) could be, according to Massimiliano Zattera's 12-slot sequence:
sheol keedy
sheo lkeedy
she olkeedy
These 6 words exist in the VM: it is unclear where the space should be inserted. There could be a rule, for example, to choose the first of the three because it maximizes the length of the first word, but we don't know.
Maybe a non-ambiguous slot sequence, allowing a unique re-parsing from a space-less transliteration, would better capture the partial ordering principle at work in the VM.