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we have some cases where the segmentation failed, since before using ipa2tokens, the spaces were not converted to "_", which is the usual way to use the function. Note that ipa2tokens splits as a default on white-space, which has advantages when using the function internally, but requires to remove whitespace by replacing by either nothing or by the underscore prior to initial segmentation.
I estimate that there are about 20 errors in the data (judging on eyeballing), and we can tolerate it, but it is important, in case you apply ipa2tokens in the future, that you keep in mind how the function works. I might add an additional comment to lingpy.org, but I think that basically the description is exhaustive enough already. The best way to prepare data anyway is to use orthography profiles. This is how we arrived at the segmentation of PN data and also ST data. But it requires, of course, more user-input than using lingpy for the segmentation task...
The text was updated successfully, but these errors were encountered:
we have some cases where the segmentation failed, since before using
ipa2tokens
, the spaces were not converted to "_", which is the usual way to use the function. Note thatipa2tokens
splits as a default on white-space, which has advantages when using the function internally, but requires to remove whitespace by replacing by either nothing or by the underscore prior to initial segmentation.I estimate that there are about 20 errors in the data (judging on eyeballing), and we can tolerate it, but it is important, in case you apply ipa2tokens in the future, that you keep in mind how the function works. I might add an additional comment to lingpy.org, but I think that basically the description is exhaustive enough already. The best way to prepare data anyway is to use orthography profiles. This is how we arrived at the segmentation of PN data and also ST data. But it requires, of course, more user-input than using lingpy for the segmentation task...
The text was updated successfully, but these errors were encountered: