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Comparing Strings (Street Names) With Machine Learning
2 projects | dev.to | 11 Nov 2021
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.
How to match names which differ slightly?
1 project | reddit.com/r/learnpython | 6 Sep 2021
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.
What are some alternatives?
fuzzywuzzy - Fuzzy String Matching in Python
TextDistance - Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
Levenshtein - The Levenshtein Python C extension module contains functions for fast computation of Levenshtein distance and string similarity
PLY - Python Lex-Yacc
pydantic - Data parsing and validation using Python type hints
sqlparse - A non-validating SQL parser module for Python
pyparsing - Python library for creating PEG parsers [Moved to: https://github.com/pyparsing/pyparsing]
kotlin-latex-listing - A syntax highlighting template for the Kotlin language in LaTeX listings.
Rouge - A pure Ruby code highlighter that is compatible with Pygments
Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
Python Left-Right Parser - Python Parser