Answerable
Recommendation system for Stack Overflow unanswered questions (by MiguelMJ)
rake-nltk
Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK. (by csurfer)
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| Answerable | rake-nltk | |
|---|---|---|
| 1 | 4 | |
| 15 | 1,034 | |
| - | - | |
| 1.8 | 0.0 | |
| almost 3 years ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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.
Answerable
Posts with mentions or reviews of Answerable.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-15.
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I made a recommendation system for Stack Overflow unanswered questions
git clone https://github.com/MiguelMJ/Answerable.git
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.
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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.
What are some alternatives?
When comparing Answerable and rake-nltk you can also consider the following projects:
debuggy - A python tool for automatically catching compiler runtime errors, parsing errors through popular discussion forums (eg. stackoverflow) and getting solutions on the terminal
yake - Single-document unsupervised keyword extraction