jellyfish VS Levenshtein

For sounds something like https://github.com/jamesturk/jellyfish ?

When comparing strings (in our case street names), there are plenty of off-the-shelf features that can be used, such as those provided by the jellyfish. This package also provides a number of phonetic encodings. We can combine an encoding with a metric, such as Levenshtein Distance, to measure the phonetic similarity between two street names.

You can use a library like jellyfish which implements a bunch of string comparison algorithms, you'd just have to experiment and see which one gives the best results for you. I think I've had the best luck with Jaro-Winkler, then looking at the % match result and picking a cutoff above which I have good confidence that the match is real. It's still not perfect, and I really don't see how your last example would work with just about any automated comparison.

Yeah my hunch is that a combination of nltk, python-Levenshtein, numpy for language processing, pandas for gathering results and scrapy for web scraping should make it possible. Sadly such a project probably requires at least a month or two worth of training in Python to prototype. Good luck OP.

I've used fuzzy-wuzzy and it is pretty slow if you can't install python-Levenshtein (which I couldn't, though I don't remember why). I ended up uninstalling it and using a custom matching algorithm for search in my app.