Semanlink - RAKE

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RAKE

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6 Documents (Long List)

Keywords2vec

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2019-02-09 About

Jeremy Howard on Twitter: "Such a ridiculously simple idea couldn't possibly work, could it? Or... could it? "

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2019-02-09 About

Salmon Run: Implementing the RAKE Algorithm with NLTK

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2017-06-26 About

RAKE: A python implementation of the Rapid Automatic Keyword Extraction

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2017-06-26 About

Automated Keyword Extraction – TF-IDF, RAKE, and TextRank (Less Than Dot - Blog)

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2017-06-13 About

NLP keyword extraction tutorial with RAKE and Maui

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2 tools:

- simple keyword extraction with a Python library (RAKE)
- Java tool (Maui) that uses a machine-learning technique.

Focus on 2 tasks:

- Extracting the most significant words and phrases that appear in given text
- Identifying a set of topics from a predefined vocabulary that match a given text

Typical steps:

- Candidate selection (extract all possible words, phrases, terms or concepts that can potentially be keywords).
- Properties calculation (for each candidate, properties that indicate that it may be a keyword)
- Scoring and selecting keywords

RAKE: finding multi-word phrases containing frequent words. +: simplicity, ease of use -: limited accuracy, parameter configuration requirement, throws away many valid phrases, doesn’t normalize candidates (no stemming).

Maui: ("Multi-purpose automatic topic indexing"). Based on [Weka](/semanlink/tag/weka) (GPL, java, maven, github). Compared to RAKE:

- Extract keywords not just from text, but also with a reference to a controlled vocabulary
- Improve the accuracy by training Maui on manually chosen keywords
    - but requires a training model.

Maui can use a controlled vocabulary expressed in SKOS - so I could use it in semanlink!

2017-05-24 About

Properties

sl:creationDate : 2017-06-14
sl:creationTime : 2017-06-14T00:24:24Z
rdf:type : sl:Tag
skos:prefLabel : RAKE