simple_keyword_clusterer
A simple machine learning package to cluster keywords in higher-level groups. (by andrea-dagostino)
yake
Single-document unsupervised keyword extraction (by LIAAD)
| simple_keyword_clusterer | yake | |
|---|---|---|
| 2 | 5 | |
| 15 | 1,573 | |
| - | 0.7% | |
| 0.0 | 3.0 | |
| almost 2 years ago | 4 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
simple_keyword_clusterer
Posts with mentions or reviews of simple_keyword_clusterer.
We have used some of these posts to build our list of alternatives
and similar projects.
yake
Posts with mentions or reviews of yake.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-14.
- Show HN: Whisper.cpp and YAKE to Analyse Voice Reflections [iOS]
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Simplest keyword extractor
Personally I prefer using YAKE.
- What method should be used to tag specific texts, when the dataset is too small for training a model?
- Is there any YAKE (yet another keyword extractor) implementation in R? Unsupervised Approach for Automatic Keyword Extraction using Text Statistical Features.
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Alternate approaches to TF-IDF?
You can look for usage here: https://github.com/LIAAD/yake and there is also a reference section with publications for more details of how this works. From what I remember, each keyphrase candidate is assigned an aggregated score based on various features: position in the text, casing, frequency, surrounding text frequency...
What are some alternatives?
When comparing simple_keyword_clusterer and yake you can also consider the following projects:
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
KeyBERT - Minimal keyword extraction with BERT
flashtext - Extract Keywords from sentence or Replace keywords in sentences.
pke - Python Keyphrase Extraction module
scattertext - Beautiful visualizations of how language differs among document types.