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YAKE! Collection-Independent Automatic Keyword Extractor

  • Ricardo Campos
  • Vítor Mangaravite
  • Arian Pasquali
  • Alípio Mário Jorge
  • Célia Nunes
  • Adam Jatowt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)

Abstract

In this paper, we present YAKE!, a novel feature-based system for multi-lingual keyword extraction from single documents, which supports texts of different sizes, domains or languages. Unlike most systems, YAKE! does not rely on dictionaries or thesauri, neither it is trained against any corpora. Instead, we follow an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in many different languages without the need for external knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training corpora is either limited or restricted. In this demo, we offer an easy to use, interactive session, where users from both academia and industry can try our system, either by using a sample document or by introducing their own text. As an add-on, we compare our extracted keywords against the output produced by the IBM Natural Language Understanding (IBM NLU) and Rake system. YAKE! demo is available at http://bit.ly/YakeDemoECIR2018. A python implementation of YAKE! is also available at PyPi repository (https://pypi.python.org/pypi/yake/).

Keywords

Keyword extraction Information extraction Text mining 

Notes

Acknowledgements

This work is partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013 and of project UID/MAT/00212/2013. It was also financed by MIC SCOPE (171507010) and by Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” which is financed by the NORTE 2020, under the Portugal 2020, and through the ERDF.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Polytechnic Institute of TomarTomarPortugal
  2. 2.LIAAD – INESC TECPortoPortugal
  3. 3.DCC – FCUPUniversity of PortoPortoPortugal
  4. 4.University of Beira InteriorCovilhãPortugal
  5. 5.Kyoto UniversityKyotoJapan

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