Earlier quoted context omitted.
I used that strategy for parsing gigabytes of CSVs containing arbitrary natural language from the web - try to get these files fixed, or figure out a grammar for gigabytes of fuzzy data... My approach never failed for me, so telling me that my strategy does not work is a strong claim, where it reliably did the job for me. Your examples are all valid, but what you are describing are theoretical attacks on the method,…
How do you know that the strategy worked reliably if you never compared the results to the results obtained using a reliable method (which you presumably didn't, because then you could just have used the reliable method)? The larger the data you have to deal with, the more likely it is that corner cases will occur in it, and the less likely that you will notice anomalies, thus the more important that you are very str…
As I wrote above, by making sure that I use a placeholder that does not appear in the data, I make sure that it does not cause the issues you describe. And if I was wrong with that assumption, I can at least minimize the effect by choosing a very unlikely sequence as placeholder.
I really see no issue here. How do you find valid grammars for fuzzy data in practice?