yake


Nameyake JSON
Version 0.4.8 PyPI version JSON
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home_pagehttps://pypi.python.org/pypi/yake
SummaryKeyword extraction Python package
upload_time2021-04-26 22:52:31
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licenseLGPLv3
keywords yake
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# 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 <a href="http://yake.inesctec.pt" target="_blank">demo</a>, an <a href="http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords" target="_blank">API</a> and a <a href="https://play.google.com/store/apps/details?id=com.yake.yake" target="_blank">mobile app</a>.

## 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](https://play.google.com/store/apps/details?id=com.yake.yake).

## References
Please cite the following works when using YAKE

<b>In-depth journal paper at Information Sciences Journal</b>

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](https://doi.org/10.1016/j.ins.2019.09.013)

<b>ECIR'18 Best Short Paper</b>

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](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63)

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](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80)

## Awards
[ECIR'18](http://ecir2018.org) Best Short Paper

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