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EmbedRank uses Maximal Margin Relevance to select the resulting keywords which is an interesting technique to diversify the selected candidates.
It should be noted that if MMR is used, then KeyBERT is essentially EmbedRank with BERT and without the selection of candidate phrases based on part-of-speech sequences.
However, the base implementation will likely, as a default, use simple cosine similarity to keep the usage without too many parameters.
The text was updated successfully, but these errors were encountered:
EmbedRank uses Maximal Margin Relevance to select the resulting keywords which is an interesting technique to diversify the selected candidates.
It should be noted that if MMR is used, then KeyBERT is essentially EmbedRank with BERT and without the selection of candidate phrases based on part-of-speech sequences.
However, the base implementation will likely, as a default, use simple cosine similarity to keep the usage without too many parameters.
The text was updated successfully, but these errors were encountered: