Post by account_disabled on Feb 27, 2024 4:57:13 GMT -5
The a tag or tag phrase was important enough to have an article dedicated to it then the tag was more likely to be a valuable term vs. random entry or keyword stuffing by the user. Further the methodology allows for grouping related terms without any bias on word order. Doing a search on Wikipedia creates a search results page pontoon boats or redirects you to a correction of the article disneyworld becomes Walt Disney World. Wikipedia also tends to have entries for some pop culture references so things that would get flagged as a misspelling such as lolcats can be vindicated by the existence of a matching Wikipedia article.
Limitations While Wikipedia is effective at delivering a Kazakhstan Phone Number consistent formal tag for disambiguation it can at times be more sterile than userfriendly. This can run counter to other signals such as CPC or traffic volume methods. For example pontoon boats becomes Pontoon Boat or Lily becomes lilium. All signals indicate the former case as the most popular but Wikipedia disambiguation suggests the latter to be the correct usage. Wikipedia also contains entries for very broad terms like each number year letter etc. so simply applying a rule that any.
Wikipedia article is an allowed tag would continue to contribute to tag sprawl problems. K Finally we attempted to transform the tags into a subset of more meaningful tags using word embeddings and kmeans clustering. Generally the process involved transforming the tags into tokens individual words then refining by partofspeech noun verb adjective and finally lemmatizing the tokens blue shirts becomes blue shirt. From there we transformed all the tokens into a custom WordVec embedding model based on adding the vectors of each resulting token array. We created a label array and a vector array of each tag in the dataset then ran kmeans with percent of the total count of the tags as the value for number of.
Limitations While Wikipedia is effective at delivering a Kazakhstan Phone Number consistent formal tag for disambiguation it can at times be more sterile than userfriendly. This can run counter to other signals such as CPC or traffic volume methods. For example pontoon boats becomes Pontoon Boat or Lily becomes lilium. All signals indicate the former case as the most popular but Wikipedia disambiguation suggests the latter to be the correct usage. Wikipedia also contains entries for very broad terms like each number year letter etc. so simply applying a rule that any.
Wikipedia article is an allowed tag would continue to contribute to tag sprawl problems. K Finally we attempted to transform the tags into a subset of more meaningful tags using word embeddings and kmeans clustering. Generally the process involved transforming the tags into tokens individual words then refining by partofspeech noun verb adjective and finally lemmatizing the tokens blue shirts becomes blue shirt. From there we transformed all the tokens into a custom WordVec embedding model based on adding the vectors of each resulting token array. We created a label array and a vector array of each tag in the dataset then ran kmeans with percent of the total count of the tags as the value for number of.