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-rw-r--r-- | README.md | 10 |
1 files changed, 2 insertions, 8 deletions
@@ -28,7 +28,7 @@ Corresponding medium post can be found [here](). ## 1. About the Project [Back to ToC](#toc) -Although that are already many methods available for keyword generation +Although there are already many methods available for keyword generation (e.g., [Rake](https://github.com/aneesha/RAKE), [YAKE!](https://github.com/LIAAD/yake), TF-IDF, etc.) @@ -51,12 +51,6 @@ papers and solutions out there that use BERT-embeddings ), I could not find a BERT-based solution that did not have to be trained from scratch and could be used for beginners (**correct me if I'm wrong!**). Thus, the goal was a `pip install keybert` and at most 3 lines of code in usage. - -**NOTE**: If you use MMR to select the candidates instead of simple cosine similarity, -this repo is essentially a simplified implementation of -[EmbedRank](https://github.com/swisscom/ai-research-keyphrase-extraction) -with BERT-embeddings. - <a name="gettingstarted"/></a> ## 2. Getting Started @@ -171,7 +165,7 @@ The results with **low diversity**: ## References Below, you can find several resources that were used for the creation of KeyBERT -but most importantly, are amazing resources for creating impressive keyword extraction models: +but most importantly, these are amazing resources for creating impressive keyword extraction models: **Papers**: * Sharma, P., & Li, Y. (2019). [Self-Supervised Contextual Keyword and Keyphrase Retrieval with Self-Labelling.](https://www.preprints.org/manuscript/201908.0073/download/final_file) |