Post by account_disabled on Feb 27, 2024 8:57:06 GMT -1
The without union. JaroWinkler distance Method There are several edit distance and string similarity metrics that we used throughout this process. Edit Distance is simply some measurement of how difficult it is to change one word to another. For example the most basic edit distance metric Levenshtein distance between Russ Jones and Russell Jones is you have to add EL and L to transform Russ to Russell. This can be used to help us find similar words and phrases. In our case we used a particular edit distance measure called JaroWinkler distance which gives higher precedence to words and phrases that are similar at the beginning.
For example Baseball would be closer to Baseballer than to Basketball Kazakhstan Phone Number because the differences are at the very end of the term. Benefits Edit distance metrics helped us find many very similar variants of tags especially when the variants were not necessarily misspellings. This was particularly valuable when used in conjunction with the Jaccard index metrics because we could apply a characterlevel metric on top of a characteragnostic metric i.e. one that cares about the letters in the tag and one that doesnt. Limitations Edit distance metrics can be kind of stupid. and Basketball are far more related to one another than Baseball and Pitcher or Catcher. Round and Circle have a horrible edit distance metric while Round and Pound look very similar. Edit distance simply cannot be used in isolation to find similar tags.
Keyword Planner grouping Method While Googles choice to combine similar keywords in Keyword Planner has been problematic for predicting traffic it has actually offered us a new method to identify highly related terms. Whenever two tags share identical metrics from Google Keyword Planner average monthly traffic historical traffic CPC and competition we can conclude that there is an increased chance the two are related to one another. Benefits This method is extremely useful for acronyms which are particularly difficult to detect.
For example Baseball would be closer to Baseballer than to Basketball Kazakhstan Phone Number because the differences are at the very end of the term. Benefits Edit distance metrics helped us find many very similar variants of tags especially when the variants were not necessarily misspellings. This was particularly valuable when used in conjunction with the Jaccard index metrics because we could apply a characterlevel metric on top of a characteragnostic metric i.e. one that cares about the letters in the tag and one that doesnt. Limitations Edit distance metrics can be kind of stupid. and Basketball are far more related to one another than Baseball and Pitcher or Catcher. Round and Circle have a horrible edit distance metric while Round and Pound look very similar. Edit distance simply cannot be used in isolation to find similar tags.
Keyword Planner grouping Method While Googles choice to combine similar keywords in Keyword Planner has been problematic for predicting traffic it has actually offered us a new method to identify highly related terms. Whenever two tags share identical metrics from Google Keyword Planner average monthly traffic historical traffic CPC and competition we can conclude that there is an increased chance the two are related to one another. Benefits This method is extremely useful for acronyms which are particularly difficult to detect.