Goodlookup delivers the benefits of AI language models to regular people.
The main limitation of traditional fuzzy matching is that it doesn’t consider similarities or cultural references outside of the text strings. Automated fuzzy matching and topic clustering require semantic understanding. Fortunately, recent advances in NLP technology have unlocked new possibilities with text data.
Goodlookup is a smart function for spreadsheet users that gets very close to semantic understanding. It’s a pre-trained model that has the intuition of GPT-3 and the join capabilities of fuzzy matching. Furthermore, it provides confidence scores so users can gauge how well the matches fit. Use it like vlookup or index match to speed up your topic clustering work in google sheets.
Based on our record, FuzzyWuzzy seems to be more popular. It has been mentiond 11 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 1 year ago
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: over 1 year ago
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: almost 2 years ago
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago
To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: almost 3 years ago
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