rake-nltk
pke
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| rake-nltk | pke | |
|---|---|---|
| 4 | 3 | |
| 1,034 | 1,524 | |
| - | - | |
| 0.0 | 3.1 | |
| over 1 year ago | 10 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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rake-nltk
- 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.
pke
- Question on easing comprehension
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[P] Building model to extract keywords from legal documents
Look into rake, pke, phrasemachine, pyate, keybert.
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Best approach for automatic key word extraction
There are lots of off-the-shelf tools for this. Look into: - https://github.com/boudinfl/pke - https://github.com/kevinlu1248/pyate - https://github.com/zelandiya/RAKE-tutorial - https://github.com/slanglab/phrasemachine - https://github.com/MaartenGr/KeyBERT/
What are some alternatives?
yake - Single-document unsupervised keyword extraction
KeyBERT - Minimal keyword extraction with BERT
NLTK - NLTK Source
flashtext - Extract Keywords from sentence or Replace keywords in sentences.
pytextrank - Python implementation of TextRank algorithms ("textgraphs") for phrase extraction
WordDumb - A calibre plugin that generates Kindle Word Wise and X-Ray files for KFX, AZW3, MOBI and EPUB eBook.
pyate - PYthon Automated Term Extraction
hepscrape - arXiv:hep-ph scraper
textstat - :memo: python package to calculate readability statistics of a text object - paragraphs, sentences, articles.
simple_keyword_clusterer - A simple machine learning package to cluster keywords in higher-level groups.
phrasemachine - Quickly extract multi-word phrases from a corpus