Semanlink - General NLP tasks AND Programming language

- 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

An Efficient Way to Extract the Main Topics from a Sentence | The Tokenizer

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2017-05-24 About

Extract Subject Matter of Documents Using NLP – Alexander Crosson – Medium

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2017-05-24 About

Sample pipeline for text feature extraction and evaluation — scikit-learn documentation

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2016-01-12 About

Working With Text Data — scikit-learn documentation

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2015-10-21 About