rake-nltk
Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK. (by csurfer)
yake
Single-document unsupervised keyword extraction (by LIAAD)
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| rake-nltk | yake | |
|---|---|---|
| 4 | 5 | |
| 1,034 | 1,571 | |
| - | 1.3% | |
| 0.0 | 3.0 | |
| over 1 year 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.
rake-nltk
Posts with mentions or reviews of rake-nltk.
We have used some of these posts to build our list of alternatives
and similar projects.
- rake-nltk 1.0.6 released. Comes with the flexibility to choose your own sentence and word tokenizers.
-
PMI for WordClouds
I'm not sure what you mean by tokenizing phrases or concepts. Specifically extracting institution names would fall under NER. You can do this with spaCy. Extracting commonly used phrases would fall under keyword extraction. For this, you can study frequencies of n-grams of length > 1 and optionally filter based on POS (i.e. NOUN+ADJ). I've never used RAKE (https://github.com/csurfer/rake-nltk) but I've heard this is also a popular method.
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]
-
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 rake-nltk and yake you can also consider the following projects:
pke - Python Keyphrase Extraction module
KeyBERT - Minimal keyword extraction with BERT
NLTK - NLTK Source
flashtext - Extract Keywords from sentence or Replace keywords in sentences.
simple_keyword_clusterer - A simple machine learning package to cluster keywords in higher-level groups.
WordDumb - A calibre plugin that generates Kindle Word Wise and X-Ray files for KFX, AZW3, MOBI and EPUB eBook.
hepscrape - arXiv:hep-ph scraper
scattertext - Beautiful visualizations of how language differs among document types.
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.