To help you get started, we've collected the most common ways that yake is being used within popular public projects.

LIAAD / yake / tests / test_yake.py View on Github

mindshare in this community, too    (though it already has plenty of that thanks to Tensorflow
    and other projects).    Kaggle has a bit of a history with Google, too, but that's pretty recent.
    Earlier this month,    Google and Kaggle teamed up to host a $100,000 machine learning competition
    around classifying    YouTube videos. That competition had some deep integrations with the
    Google Cloud Platform, too.    Our understanding is that Google will keep the service running -
    likely under its current name.    While the acquisition is probably more about Kaggle's community
    than technology, Kaggle did build    some interesting tools for hosting its competition and 'kernels',
    too. On Kaggle, kernels are    basically the source code for analyzing data sets and developers can
    share this code on the    platform (the company previously called them 'scripts').  Like similar
    competition-centric sites,    Kaggle also runs a job board, too. It's unclear what Google will do
    with that part of the service.    According to Crunchbase, Kaggle raised $12.5 million (though PitchBook
    says it's $12.75) since its    launch in 2010. Investors in Kaggle include Index Ventures, SV Angel,
    Max Levchin, Naval Ravikant,    Google chief economist Hal Varian, Khosla Ventures and Yuri Milner
    """

    pyake = yake.KeywordExtractor(lan="en",n=3)

    result = pyake.extract_keywords(text_content)

    print(result)

    keywords = [kw[0] for kw in result]

    print(keywords)
    assert "google" in keywords
    assert "kaggle" in keywords
    assert "san francisco" in keywords
    assert "machine learning" in keywords

View more ways to use yake

Yet Another Keyword Extractor (Yake)

Unsupervised Approach for Automatic Keyword Extraction using Text Features.

YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Experimental results carried out on top of twenty datasets (see Benchmark section below) show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. In addition to the python package here described, we also make available a demo, an API and a mobile app.

Main Features

• Unsupervised approach
• Corpus-Independent
• Domain and Language Independent
• Single-Document

Where can I find YAKE!?

YAKE! is available online [http://yake.inesctec.pt], as an open source Python package [https://github.com/LIAAD/yake] and on Google Play.

References

Please cite the following works when using YAKE

In-depth journal paper at Information Sciences Journal

Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. pdf

ECIR'18 Best Short Paper

Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 684 - 691. pdf

Campos R., Mangaravite V., Pasquali A., Jorge A.M., Nunes C., and Jatowt A. (2018). YAKE! Collection-independent Automatic Keyword Extractor. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds). Advances in Information Retrieval. ECIR 2018 (Grenoble, France. March 26 – 29). Lecture Notes in Computer Science, vol 10772, pp. 806 - 810. pdf

Awards

ECIR'18 Best Short Paper