Semanlink - Keyword/keyphrase extraction

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23 Documents (Long List)

MaartenGr/KeyBERT: Minimal keyword extraction with BERT

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2022-10-06 About

Ramsri Goutham Golla sur Twitter : "how you can extract keywords from any text or document using only sentence transformer vector embeddings?"

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2022-09-06 About

YAKE! Keyword extraction from single documents using multiple local features (2019)

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Keyword/keyphrase extraction

2021-06-10 About

Large-Scale Evaluation of Keyphrase Extraction Models (2020)

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Keyword/keyphrase extraction

2021-05-31 About

Keyword Extraction with BERT | Towards Data Science

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2020-12-06 About

Blackstone Concept Extractor — ICLR&D

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2019-08-21 About

Mining Quality Phrases from Massive Text Corpora (2015)

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2019-07-15 About

Wikify!: linking documents to encyclopedic knowledge (2007)

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2019-04-16 About

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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

Key topics extraction and contextual sentiment of users’ reviews

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2018-09-18 About

Automatic Keyphrase Extraction: A Survey of the State of the Art (2014)

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2018-08-10 About

gensim: models.phrases – Phrase (collocation) detection

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2017-07-10 About

A Ranking Approach to Keyphrase Extraction - Microsoft Research (2009)

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2017-06-27 About

Kea (Keyphrase Extraction Algorithm)

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Keyword/keyphrase extraction

2017-06-26 About

Automatic Keyphrase Extraction (Poster): A Survey of the State of the Art (2014)

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Keyword/keyphrase extraction

2017-06-20 About

Automatic Keyphrase Extraction: A Survey of the State of the Art (2014)

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Keyword/keyphrase extraction

2017-06-20 About

Conundrums in Unsupervised Keyphrase Extraction: Making Sense of the State-of-the-Art (2010)

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Keyword/keyphrase extraction

2017-06-14 About

Intro to Automatic Keyphrase Extraction

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2017-06-14 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

Swayy | Blog — An algorithm for generating automatic hashtags

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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

Properties

sl:creationDate : 2017-05-24
sl:creationTime : 2017-05-24T18:08:08Z
rdf:type : sl:Tag
skos:altLabel :
- Keyphrase extraction@en
- Topic extraction@en
- Keyword extraction@en
skos:prefLabel : Keyword/keyphrase extraction@en