YAKE extractor
https://github.com/LIAAD/yake
GitHub - LIAAD/yake: Single-document unsupervised keyword extraction
2022-10-19
LIAAD
<div><div>
 <article><h2>Yet Another Keyword Extractor (Yake)</h2>
<p>Unsupervised Approach for Automatic Keyword Extraction using Text Features.</p>
<p>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/">demo</a>, an <a href="http://yake.inesctec.pt/apidocs/#!/available_methods/post_yake_v2_extract_keywords">API</a> and a <a href="https://play.google.com/store/apps/details?id=com.yake.yake">mobile app</a>.</p>
<h2>Main Features</h2>
<ul>
<li>Unsupervised approach</li>
<li>Corpus-Independent</li>
<li>Domain and Language Independent</li>
<li>Single-Document</li>
</ul>
<h2>Benchmark</h2>
<p>For Benchmark results check out our paper published on Information Science Journal (see the references section).</p>
<h2>Rationale</h2>
<p>Extracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents. Yake! is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further 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.</p>
<h2>Where can I find YAKE!?</h2>
<p>YAKE! is available online [<a href="http://yake.inesctec.pt/">http://yake.inesctec.pt</a>], on <a href="https://play.google.com/store/apps/details?id=com.yake.yake">Google Play</a>, as an open source Python package [<a href="https://github.com/LIAAD/yake">https://github.com/LIAAD/yake</a>] and as an <a href="http://yake.inesctec.pt/apidocs/#/available_methods/post_yake_v2_extract_keywords">API</a>.</p>
<h2>Installing YAKE!</h2>
<p>There are three installation alternatives.</p>
<ul>
<li>To run YAKE! in the command line (say, to integrate in a script), but you do not need an HTTP server on top, you can use our <a href="#cli-image">simple YAKE! Docker image</a>. This container will allow you to run text extraction as a command, and then exit.</li>
<li>To run YAKE! as an HTTP server featuring a RESTful API (say to integrate in a web application or host your own YAKE!), you can use our <a href="#rest-api-image">RESTful API server image</a>. This container/server <em>will run in the background</em>.</li>
<li>To install YAKE! straight "on the metal" or you want to integrate it in your Python app, you can <a href="#standalone-installation">install it and its dependencies</a>.</li>
</ul>
<h3>Option 1. YAKE as a CLI utility inside a Docker container</h3>
<p>First, install Docker. Ubuntu users, please see our <a href="#installing-docker">script below</a> for a complete installation script.</p>
<p>Then, run:</p>
<div><pre>docker run liaad/yake:latest -ti "Caffeine is a central nervous system (CNS) stimulant of the methylxanthine class.[10] It is the world's most widely consumed psychoactive drug. Unlike many other psychoactive substances, it is legal and unregulated in nearly all parts of the world. There are several known mechanisms of action to explain the effects of caffeine. The most prominent is that it reversibly blocks the action of adenosine on its receptor and consequently prevents the onset of drowsiness induced by adenosine. Caffeine also stimulates certain portions of the autonomic nervous system."</pre></div>
<p><em>Example text from Wikipedia</em></p>
<h3>Option 2. REST API Server in a Docker container</h3>
<p>This install will provide you a mirror of the original REST API of YAKE! available <a href="https://boiling-castle-88317.herokuapp.com/">here</a>.</p>
<div><pre>docker run -p 5000:5000 -d liaad/yake-server:latest</pre></div>
<p>After it starts up, the container will run in the background, at <a href="http://127.0.0.1:5000/">http://127.0.0.1:5000</a>. To access the YAKE! API documentation, go to <a href="http://127.0.0.1:5000/apidocs/">http://127.0.0.1:5000/apidocs/</a>.</p>
<p>You can test the RESTful API using <code>curl</code>:</p>
<div><pre>curl -X POST "http://localhost:5000/yake/" -H "accept: application/json" -H "Content-Type: application/json" \
-d @- &lt;&lt;'EOF'
{
 "language": "en",
 "max_ngram_size": 3,
 "number_of_keywords": 10,
 "text": "Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists ..."
}
EOF</pre></div>
<p><em>Example text from Wikipedia</em></p>
<h3>Option 3. Standalone Installation (for development or integration)</h3>
<h4>Requirements</h4>
<p>Python3</p>
<h4>Installation</h4>
<p>To install Yake using pip:</p>
<div><pre>pip install git+https://github.com/LIAAD/yake</pre></div>
<p>To upgrade using pip:</p>
<div><pre>pip install git+https://github.com/LIAAD/yake –-upgrade</pre></div>
<h4>Usage (Command line)</h4>
<p>How to use it on your favorite command line</p>
<div><pre>Usage: yake [OPTIONS]
Options:
	-ti, --text_input TEXT Input text, SURROUNDED by single quotes(')
	-i, --input_file TEXT Input file
	-l, --language TEXT Language
	-n, --ngram-size INTEGER Max size of the ngram.
	-df, --dedup-func [leve|jaro|seqm]
	Deduplication function.
	-dl, --dedup-lim FLOAT Deduplication limiar.
	-ws, --window-size INTEGER Window size.
	-t, --top INTEGER Number of keyphrases to extract
	-v, --verbose	Gets detailed information (such as the score)
	--help Show this message and exit.</pre></div>
<h3>Usage (Python)</h3>
<p>How to use it on Python</p>
<div><pre>import yake
text = "Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning "\
"competitions. Details about the transaction remain somewhat vague, but given that Google is hosting its Cloud "\
"Next conference in San Francisco this week, the official announcement could come as early as tomorrow. "\
"Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. "\
"Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, "\
"was founded by Goldbloom and Ben Hamner in 2010. "\
"The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, "\
"it has managed to stay well ahead of them by focusing on its specific niche. "\
"The service is basically the de facto home for running data science and machine learning competitions. "\
"With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, "\
"it will get increased 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 "</pre></div>
<h4>assuming default parameters</h4>
<div><pre>kw_extractor = yake.KeywordExtractor()
keywords = kw_extractor.extract_keywords(text)
for kw in keywords:
	print(kw)</pre></div>
<h4>specifying parameters</h4>
<div><pre>language = "en"
max_ngram_size = 3
deduplication_threshold = 0.9
deduplication_algo = 'seqm'
windowSize = 1
numOfKeywords = 20
custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, dedupLim=deduplication_threshold, dedupFunc=deduplication_algo, windowsSize=windowSize, top=numOfKeywords, features=None)
keywords = custom_kw_extractor.extract_keywords(text)
for kw in keywords:
 print(kw)</pre></div>
<h4>Output</h4>
<p>The lower the score, the more relevant the keyword is.</p>
<div><pre>('google', 0.026580863364597897)
('kaggle', 0.0289005976239829)
('ceo anthony goldbloom', 0.029946071606210194)
('san francisco', 0.048810837074825336)
('anthony goldbloom declined', 0.06176910090701819)
('google cloud platform', 0.06261974476422487)
('co-founder ceo anthony', 0.07357749587020043)
('acquiring kaggle', 0.08723571551039863)
('ceo anthony', 0.08915156857226395)
('anthony goldbloom', 0.09123482372372106)
('machine learning', 0.09147989238151344)
('kaggle co-founder ceo', 0.093805063905847)
('data', 0.097574333771058)
('google cloud', 0.10260128641464673)
('machine learning competitions', 0.10773000650607861)
('francisco this week', 0.11519915079240485)
('platform', 0.1183512305596321)
('conference in san', 0.12392066376108138)
('service', 0.12546743261462942)
('goldbloom', 0.14611408778815776)</pre></div>
<h3>Highlighting Feature</h3>
<p>Highlighting feature will tag every keyword in the text with the default tag <code>&lt;kw&gt;</code>.</p>
<div><pre>from yake.highlight import TextHighlighter
th = TextHighlighter(max_ngram_size = 3)
th.highlight(text, keywords)</pre></div>
<h4>Output</h4>
<p>By default, keywords will be highlighted using the tag 'kw'.</p>
<div><pre><code>Sources tell us that &lt;kw&gt;google&lt;/kw&gt; is &lt;kw&gt;acquiring kaggle&lt;/kw&gt;, a platform that &lt;kw&gt;hosts data science&lt;/kw&gt; and &lt;kw&gt;machine learning&lt;/kw&gt; competitions. Details about the transaction remain somewhat vague , but given that &lt;kw&gt;google&lt;/kw&gt; is hosting its Cloud Next conference in &lt;kw&gt;san francisco&lt;/kw&gt; this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder &lt;kw&gt;ceo anthony goldbloom&lt;/kw&gt; declined to deny that the acquisition is happening. &lt;kw&gt;google&lt;/kw&gt; itself declined 'to comment on rumors'.
.....
.....
</code></pre></div>
<h3>Custom Highlighting Feature</h3>
<p>Besides tagging a text with the default tag, users can also specify their own custom highlight. In the following text, the tag <code>&lt;span class='my_class' &gt;</code> makes use of an hyphotetical function <code>my_class</code> whose purpose would be to highlight in white colour or the relevant keywords.</p>
<h4>Output</h4>
<div><pre>from yake.highlight import TextHighlighter
th = TextHighlighter(max_ngram_size = 3, highlight_pre = "&lt;span class='my_class' &gt;", highlight_post= "&lt;/span&gt;")
th.highlight(text, keywords)</pre></div>
<div><pre><code>self.highlight_postSources tell us that &lt;span class='my_class' &gt;google&lt;/span&gt; is &lt;span class='my_class' &gt;acquiring kaggle&lt;/span&gt;, a platform that &lt;span class='my_class' &gt;hosts data science&lt;/span&gt; and &lt;span class='my_class' &gt;machine learning&lt;/span&gt; self.highlight_postcompetitions. Details about the transaction remain somewhat vague , but given that &lt;span class='my_class' &gt;google&lt;/span&gt; is hosting self.highlight_postits Cloud Next conference in &lt;span class='my_class' &gt;san francisco&lt;/span&gt; this week, the official announcement could come as early self.highlight_postas tomorrow. Reached by phone, Kaggle co-founder &lt;span class='my_class' &gt;ceo anthony goldbloom&lt;/span&gt; declined to deny that the self.highlight_postacquisition is happening. &lt;span class='my_class' &gt;google&lt;/span&gt; itself declined 'to comment on rumors'.
.....
.....
</code></pre></div>
<h3>Languages others than English</h3>
<p>While English (<code>en</code>) is the default language, users can use YAKE! to extract keywords from whatever language they want to by specifying the the corresponding language universal code. The below example shows how to extract keywords from a portuguese text.</p>
<div><pre>text = '''
"Conta-me Histórias." Xutos inspiram projeto premiado. A plataforma "Conta-me Histórias" foi distinguida com o Prémio Arquivo.pt, atribuído a trabalhos inovadores de investigação ou aplicação de recursos preservados da Web, através dos serviços de pesquisa e acesso disponibilizados publicamente pelo Arquivo.pt . Nesta plataforma em desenvolvimento, o utilizador pode pesquisar sobre qualquer tema e ainda executar alguns exemplos predefinidos. Como forma de garantir a pluralidade e diversidade de fontes de informação, esta são utilizadas 24 fontes de notícias eletrónicas, incluindo a TSF. Uma versão experimental (beta) do "Conta-me Histórias" está disponível aqui.
A plataforma foi desenvolvida por Ricardo Campos investigador do LIAAD do INESC TEC e docente do Instituto Politécnico de Tomar, Arian Pasquali e Vitor Mangaravite, também investigadores do LIAAD do INESC TEC, Alípio Jorge, coordenador do LIAAD do INESC TEC e docente na Faculdade de Ciências da Universidade do Porto, e Adam Jatwot docente da Universidade de Kyoto.
'''
custom_kw_extractor = yake.KeywordExtractor(lan="pt")
keywords = custom_kw_extractor.extract_keywords(text)
for kw in keywords:
 print(kw)</pre></div>
<h4>Output</h4>
<div><pre>('conta-me histórias', 0.006225012963810038)
('liaad do inesc', 0.01899063587015275)
('inesc tec', 0.01995432290332246)
('conta-me', 0.04513273690417472)
('histórias', 0.04513273690417472)
('prémio arquivo.pt', 0.05749361520927859)
('liaad', 0.07738867367929901)
('inesc', 0.07738867367929901)
('tec', 0.08109398065524037)
('xutos inspiram projeto', 0.08720742489353424)
('inspiram projeto premiado', 0.08720742489353424)
('adam jatwot docente', 0.09407053486771558)
('arquivo.pt', 0.10261392141666957)
('alípio jorge', 0.12190479662535166)
('ciências da universidade', 0.12368384021490342)
('ricardo campos investigador', 0.12789997272332762)
('politécnico de tomar', 0.13323587141127738)
('arian pasquali', 0.13323587141127738)
('vitor mangaravite', 0.13323587141127738)
('preservados da web', 0.13596322680882506)</pre></div>
<h2>Related projects</h2>
<h3>YAKE! Mobile APP</h3>
<p>YAKE! is now available on <a href="https://play.google.com/store/apps/details?id=com.yake.yake">Google Play</a></p>
<h3><code>pke</code> - python keyphrase extraction</h3>
<p><a href="https://github.com/boudinfl/pke">https://github.com/boudinfl/pke</a> - <code>pke</code> is an <strong>open source</strong> python-based <strong>keyphrase extraction</strong> toolkit. It
provides an end-to-end keyphrase extraction pipeline in which each component can
be easily modified or extended to develop new models. <code>pke</code> also allows for
easy benchmarking of state-of-the-art keyphrase extraction models, and
ships with supervised models trained on the SemEval-2010 dataset (<a href="http://aclweb.org/anthology/S10-1004">http://aclweb.org/anthology/S10-1004</a>).</p>
<p>Credits to <a href="https://github.com/boudinfl">https://github.com/boudinfl</a></p>
<h3><code>SparkNLP</code> - State of the Art Natural Language Processing framework</h3>
<p><a href="https://github.com/JohnSnowLabs/spark-nlp">https://github.com/JohnSnowLabs/spark-nlp</a> - <code>SparkNLP</code> from <a href="https://www.johnsnowlabs.com/">John Snow Labs</a> is an open source framework with full Python, Scala, and Java Support. Check <a href="https://nlp.johnsnowlabs.com/docs/en/annotators#yakekeywordextraction">their documentation</a>, <a href="https://demo.johnsnowlabs.com/public/KEYPHRASE_EXTRACTION/">demo</a> and <a href="https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/KEYPHRASE_EXTRACTION.ipynb">google colab</a>. A video on how to use spark nlp with yake can also be found here: <a href="https://events.johnsnowlabs.com/john-snow-labs-nlu-become-a-data-science-superhero-with-one-line-of-python-code">https://events.johnsnowlabs.com/john-snow-labs-nlu-become-a-data-science-superhero-with-one-line-of-python-code</a></p>
<h3><code>General Index</code> by Archive.org</h3>
<p><a href="https://archive.org/details/GeneralIndex">https://archive.org/details/GeneralIndex</a> - A catalogue of 19 billions of YAKE keywords extracted from 107 million papers. An article about the General Index project can also be found in <a href="https://www.nature.com/articles/d41586-021-02895-8">Nature</a>.</p>
<h3><code>textacy</code> - NLP, before and after spaCy</h3>
<p><a href="https://github.com/chartbeat-labs/textacy">https://github.com/chartbeat-labs/textacy</a> - <code>textacy</code> is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. among other features it supports keyword extration using YAKE.</p>
<p>Credits to <a href="https://github.com/chartbeat-labs">https://github.com/chartbeat-labs</a></p>
<h3><code>Annif</code> - Tool for automated subject indexing and classification</h3>
<p><a href="https://github.com/NatLibFi/Annif/">https://github.com/NatLibFi/Annif/</a> - <code>Annif</code> is a multi-algorithm automated subject indexing tool for libraries, archives and museums. This repository is used for developing a production version of the system, based on ideas from the initial prototype. Official website <a href="http://annif.org/">http://annif.org/</a>.</p>
<h3><code>Portulan Clarin</code> - Services and data for researchers, innovators, students and language professionals</h3>
<p><a href="https://portulanclarin.net/workbench/liaad-yake/">https://portulanclarin.net/workbench/liaad-yake/</a> - <code>Portulan Clarin</code> is a Research Infrastructure for the Science and Technology of Language, belonging to the Portuguese National Roadmap of Research Infrastructures of Strategic Relevance, and part of the international research infrastructure CLARIN ERIC. It includes a demo of YAKE! among many other language technologies. Official website <a href="https://portulanclarin.net/">https://portulanclarin.net/</a>.</p>
<h2>How to install Docker</h2>
<p>Here is the "just copy and paste" installations script for Docker in Ubuntu. Enjoy.</p>
<div><pre># Install dependencies
sudo apt-get update
sudo apt-get install \
 apt-transport-https \
 ca-certificates \
 curl \
 software-properties-common
# Add Docker repo
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
 "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
 $(lsb_release -cs) \
 stable"
sudo apt-get update
# Install Docker
sudo apt-get install -y docker-ce
# Start Docker Daemon
sudo service docker start
# Add yourself to the Docker user group, otherwise docker will complain that
# it does not know if the Docker Daemon is running
sudo usermod -aG docker ${USER}
# Install docker-compose
sudo curl -L "https://github.com/docker/compose/releases/download/1.23.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
source ~/.bashrc
docker-compose --version
echo "Done!"</pre></div>
<p>Credits to <a href="https://github.com/silvae86">https://github.com/silvae86</a> for the Docker scripts.</p>
<h2>References</h2>
<p>Please cite the following works when using YAKE</p>
<p><b>In-depth journal paper at Information Sciences Journal</b></p>
<p>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. <a href="https://doi.org/10.1016/j.ins.2019.09.013">pdf</a></p>
<p><b>ECIR'18 Best Short Paper</b></p>
<p>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. <a href="https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63">pdf</a></p>
<p>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. <a href="https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80">pdf</a></p>
<h2>Awards</h2>
<p><a href="http://ecir2018.org/">ECIR'18</a> Best Short Paper</p>
</article>
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 View code
 Yet Another Keyword Extractor (Yake) Main Features Benchmark Rationale Where can I find YAKE!? Installing YAKE! Option 1. YAKE as a CLI utility inside a Docker container Option 2. REST API Server in a Docker container Option 3. Standalone Installation (for development or integration) Requirements Installation Usage (Command line) Usage (Python) assuming default parameters specifying parameters Output Highlighting Feature Output Custom Highlighting Feature Output Languages others than English Output Related projects YAKE! Mobile APP pke - python keyphrase extraction SparkNLP - State of the Art Natural Language Processing framework General Index by Archive.org textacy - NLP, before and after spaCy Annif - Tool for automated subject indexing and classification Portulan Clarin - Services and data for researchers, innovators, students and language professionals How to install Docker References Awards

 README.md

 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

 Benchmark

 For Benchmark results check out our paper published on Information Science Journal (see the references section).

 Rationale

 Extracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents. Yake! is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further 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.

 Where can I find YAKE!?

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

 Installing YAKE!

 There are three installation alternatives.

 * To run YAKE! in the command line (say, to integrate in a script), but you do not need an HTTP server on top, you can use our simple YAKE! Docker image. This container will allow you to run text extraction as a command, and then exit.
 * To run YAKE! as an HTTP server featuring a RESTful API (say to integrate in a web application or host your own YAKE!), you can use our RESTful API server image. This container/server will run in the background.
 * To install YAKE! straight "on the metal" or you want to integrate it in your Python app, you can install it and its dependencies.

 Option 1. YAKE as a CLI utility inside a Docker container

 First, install Docker. Ubuntu users, please see our script below for a complete installation script.

 Then, run:

 docker run liaad/yake:latest -ti "Caffeine is a central nervous system (CNS) stimulant of the methylxanthine class.[10] It is the world's most widely consumed psychoactive drug. Unlike many other psychoactive substances, it is legal and unregulated in nearly all parts of the world. There are several known mechanisms of action to explain the effects of caffeine. The most prominent is that it reversibly blocks the action of adenosine on its receptor and consequently prevents the onset of drowsiness induced by adenosine. Caffeine also stimulates certain portions of the autonomic nervous system."

 Example text from Wikipedia

 Option 2. REST API Server in a Docker container

 This install will provide you a mirror of the original REST API of YAKE! available here.

 docker run -p 5000:5000 -d liaad/yake-server:latest

 After it starts up, the container will run in the background, at http://127.0.0.1:5000. To access the YAKE! API documentation, go to http://127.0.0.1:5000/apidocs/.

 You can test the RESTful API using curl:

 curl -X POST "http://localhost:5000/yake/" -H "accept: application/json" -H "Content-Type: application/json" \
 -d @- <<'EOF'
 {
 "language": "en",
 "max_ngram_size": 3,
 "number_of_keywords": 10,
 "text": "Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists ..."
 }
 EOF

 Example text from Wikipedia

 Option 3. Standalone Installation (for development or integration)

 Requirements

 Python3

 Installation

 To install Yake using pip:

 pip install git+https://github.com/LIAAD/yake

 To upgrade using pip:

 pip install git+https://github.com/LIAAD/yake –-upgrade

 Usage (Command line)

 How to use it on your favorite command line

 Usage: yake [OPTIONS]
 
 Options:
 	-ti, --text_input TEXT Input text, SURROUNDED by single quotes(')
 	-i, --input_file TEXT Input file
 	-l, --language TEXT Language
 	-n, --ngram-size INTEGER Max size of the ngram.
 	-df, --dedup-func [leve|jaro|seqm]
 									Deduplication function.
 	-dl, --dedup-lim FLOAT Deduplication limiar.
 	-ws, --window-size INTEGER Window size.
 	-t, --top INTEGER Number of keyphrases to extract
 	-v, --verbose			Gets detailed information (such as the score)
 	--help Show this message and exit.

 Usage (Python)

 How to use it on Python

 import yake
 
 text = "Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning "\
 "competitions. Details about the transaction remain somewhat vague, but given that Google is hosting its Cloud "\
 "Next conference in San Francisco this week, the official announcement could come as early as tomorrow. "\
 "Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. "\
 "Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, "\
 "was founded by Goldbloom and Ben Hamner in 2010. "\
 "The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, "\
 "it has managed to stay well ahead of them by focusing on its specific niche. "\
 "The service is basically the de facto home for running data science and machine learning competitions. "\
 "With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, "\
 "it will get increased 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 "

 assuming default parameters

 kw_extractor = yake.KeywordExtractor()
 keywords = kw_extractor.extract_keywords(text)
 
 for kw in keywords:
 	print(kw)

 specifying parameters

 language = "en"
 max_ngram_size = 3
 deduplication_threshold = 0.9
 deduplication_algo = 'seqm'
 windowSize = 1
 numOfKeywords = 20
 
 custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, dedupLim=deduplication_threshold, dedupFunc=deduplication_algo, windowsSize=windowSize, top=numOfKeywords, features=None)
 keywords = custom_kw_extractor.extract_keywords(text)
 
 for kw in keywords:
 print(kw)

 Output

 The lower the score, the more relevant the keyword is.

 ('google', 0.026580863364597897)
 ('kaggle', 0.0289005976239829)
 ('ceo anthony goldbloom', 0.029946071606210194)
 ('san francisco', 0.048810837074825336)
 ('anthony goldbloom declined', 0.06176910090701819)
 ('google cloud platform', 0.06261974476422487)
 ('co-founder ceo anthony', 0.07357749587020043)
 ('acquiring kaggle', 0.08723571551039863)
 ('ceo anthony', 0.08915156857226395)
 ('anthony goldbloom', 0.09123482372372106)
 ('machine learning', 0.09147989238151344)
 ('kaggle co-founder ceo', 0.093805063905847)
 ('data', 0.097574333771058)
 ('google cloud', 0.10260128641464673)
 ('machine learning competitions', 0.10773000650607861)
 ('francisco this week', 0.11519915079240485)
 ('platform', 0.1183512305596321)
 ('conference in san', 0.12392066376108138)
 ('service', 0.12546743261462942)
 ('goldbloom', 0.14611408778815776)

 Highlighting Feature

 Highlighting feature will tag every keyword in the text with the default tag <kw>.

 from yake.highlight import TextHighlighter
 
 th = TextHighlighter(max_ngram_size = 3)
 th.highlight(text, keywords)

 Output

 By default, keywords will be highlighted using the tag 'kw'.

 Sources tell us that <kw>google</kw> is <kw>acquiring kaggle</kw>, a platform that <kw>hosts data science</kw> and <kw>machine learning</kw> competitions. Details about the transaction remain somewhat vague , but given that <kw>google</kw> is hosting its Cloud Next conference in <kw>san francisco</kw> this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder <kw>ceo anthony goldbloom</kw> declined to deny that the acquisition is happening. <kw>google</kw> itself declined 'to comment on rumors'.
 .....
 .....
 

 Custom Highlighting Feature

 Besides tagging a text with the default tag, users can also specify their own custom highlight. In the following text, the tag <span class='my_class' > makes use of an hyphotetical function my_class whose purpose would be to highlight in white colour or the relevant keywords.

 Output

 from yake.highlight import TextHighlighter
 th = TextHighlighter(max_ngram_size = 3, highlight_pre = "<span class='my_class' >", highlight_post= "</span>")
 th.highlight(text, keywords)
 self.highlight_postSources tell us that <span class='my_class' >google</span> is <span class='my_class' >acquiring kaggle</span>, a platform that <span class='my_class' >hosts data science</span> and <span class='my_class' >machine learning</span> self.highlight_postcompetitions. Details about the transaction remain somewhat vague , but given that <span class='my_class' >google</span> is hosting self.highlight_postits Cloud Next conference in <span class='my_class' >san francisco</span> this week, the official announcement could come as early self.highlight_postas tomorrow. Reached by phone, Kaggle co-founder <span class='my_class' >ceo anthony goldbloom</span> declined to deny that the self.highlight_postacquisition is happening. <span class='my_class' >google</span> itself declined 'to comment on rumors'.
 .....
 .....
 

 Languages others than English

 While English (en) is the default language, users can use YAKE! to extract keywords from whatever language they want to by specifying the the corresponding language universal code. The below example shows how to extract keywords from a portuguese text.

 text = '''
 "Conta-me Histórias." Xutos inspiram projeto premiado. A plataforma "Conta-me Histórias" foi distinguida com o Prémio Arquivo.pt, atribuído a trabalhos inovadores de investigação ou aplicação de recursos preservados da Web, através dos serviços de pesquisa e acesso disponibilizados publicamente pelo Arquivo.pt . Nesta plataforma em desenvolvimento, o utilizador pode pesquisar sobre qualquer tema e ainda executar alguns exemplos predefinidos. Como forma de garantir a pluralidade e diversidade de fontes de informação, esta são utilizadas 24 fontes de notícias eletrónicas, incluindo a TSF. Uma versão experimental (beta) do "Conta-me Histórias" está disponível aqui.
 A plataforma foi desenvolvida por Ricardo Campos investigador do LIAAD do INESC TEC e docente do Instituto Politécnico de Tomar, Arian Pasquali e Vitor Mangaravite, também investigadores do LIAAD do INESC TEC, Alípio Jorge, coordenador do LIAAD do INESC TEC e docente na Faculdade de Ciências da Universidade do Porto, e Adam Jatwot docente da Universidade de Kyoto.
 '''
 
 custom_kw_extractor = yake.KeywordExtractor(lan="pt")
 keywords = custom_kw_extractor.extract_keywords(text)
 
 for kw in keywords:
 print(kw)

 Output

 ('conta-me histórias', 0.006225012963810038)
 ('liaad do inesc', 0.01899063587015275)
 ('inesc tec', 0.01995432290332246)
 ('conta-me', 0.04513273690417472)
 ('histórias', 0.04513273690417472)
 ('prémio arquivo.pt', 0.05749361520927859)
 ('liaad', 0.07738867367929901)
 ('inesc', 0.07738867367929901)
 ('tec', 0.08109398065524037)
 ('xutos inspiram projeto', 0.08720742489353424)
 ('inspiram projeto premiado', 0.08720742489353424)
 ('adam jatwot docente', 0.09407053486771558)
 ('arquivo.pt', 0.10261392141666957)
 ('alípio jorge', 0.12190479662535166)
 ('ciências da universidade', 0.12368384021490342)
 ('ricardo campos investigador', 0.12789997272332762)
 ('politécnico de tomar', 0.13323587141127738)
 ('arian pasquali', 0.13323587141127738)
 ('vitor mangaravite', 0.13323587141127738)
 ('preservados da web', 0.13596322680882506)

 Related projects

 YAKE! Mobile APP

 YAKE! is now available on Google Play

 pke - python keyphrase extraction

 https://github.com/boudinfl/pke - pke is an open source python-based keyphrase extraction toolkit. It provides an end-to-end keyphrase extraction pipeline in which each component can be easily modified or extended to develop new models. pke also allows for easy benchmarking of state-of-the-art keyphrase extraction models, and ships with supervised models trained on the SemEval-2010 dataset (http://aclweb.org/anthology/S10-1004).

 Credits to https://github.com/boudinfl

 SparkNLP - State of the Art Natural Language Processing framework

 https://github.com/JohnSnowLabs/spark-nlp - SparkNLP from John Snow Labs is an open source framework with full Python, Scala, and Java Support. Check their documentation, demo and google colab. A video on how to use spark nlp with yake can also be found here: https://events.johnsnowlabs.com/john-snow-labs-nlu-become-a-data-science-superhero-with-one-line-of-python-code

 General Index by Archive.org

 https://archive.org/details/GeneralIndex - A catalogue of 19 billions of YAKE keywords extracted from 107 million papers. An article about the General Index project can also be found in Nature.

 textacy - NLP, before and after spaCy

 https://github.com/chartbeat-labs/textacy - textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. among other features it supports keyword extration using YAKE.

 Credits to https://github.com/chartbeat-labs

 Annif - Tool for automated subject indexing and classification

 https://github.com/NatLibFi/Annif/ - Annif is a multi-algorithm automated subject indexing tool for libraries, archives and museums. This repository is used for developing a production version of the system, based on ideas from the initial prototype. Official website http://annif.org/.

 Portulan Clarin - Services and data for researchers, innovators, students and language professionals

 https://portulanclarin.net/workbench/liaad-yake/ - Portulan Clarin is a Research Infrastructure for the Science and Technology of Language, belonging to the Portuguese National Roadmap of Research Infrastructures of Strategic Relevance, and part of the international research infrastructure CLARIN ERIC. It includes a demo of YAKE! among many other language technologies. Official website https://portulanclarin.net/.

 How to install Docker

 Here is the "just copy and paste" installations script for Docker in Ubuntu. Enjoy.

 # Install dependencies
 sudo apt-get update
 sudo apt-get install \
 apt-transport-https \
 ca-certificates \
 curl \
 software-properties-common
 
 # Add Docker repo
 curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
 sudo apt-key fingerprint 0EBFCD88
 sudo add-apt-repository \
 "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
 $(lsb_release -cs) \
 stable"
 sudo apt-get update
 
 # Install Docker
 sudo apt-get install -y docker-ce
 
 # Start Docker Daemon
 sudo service docker start
 
 # Add yourself to the Docker user group, otherwise docker will complain that
 # it does not know if the Docker Daemon is running
 sudo usermod -aG docker ${USER}
 
 # Install docker-compose
 sudo curl -L "https://github.com/docker/compose/releases/download/1.23.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
 sudo chmod +x /usr/local/bin/docker-compose
 source ~/.bashrc
 docker-compose --version
 echo "Done!"

 Credits to https://github.com/silvae86 for the Docker scripts.

 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

 About

 Single-document unsupervised keyword extraction

 liaad.github.io/yake

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 keyword-extraction unsupervised-approach corpus-independent domain-and-language-independent single-document

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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 demo, an API and a mobile app.
Main Features
Unsupervised approach
Corpus-Independent
Domain and Language Independent
Single-Document
Benchmark
For Benchmark results check out our paper published on Information Science Journal (see the references section).
Rationale
Extracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents. Yake! is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further 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.
Where can I find YAKE!?
YAKE! is available online [http://yake.inesctec.pt], on Google Play, as an open source Python package [https://github.com/LIAAD/yake] and as an API.
Installing YAKE!
There are three installation alternatives.
To run YAKE! in the command line (say, to integrate in a script), but you do not need an HTTP server on top, you can use our simple YAKE! Docker image. This container will allow you to run text extraction as a command, and then exit.
To run YAKE! as an HTTP server featuring a RESTful API (say to integrate in a web application or host your own YAKE!), you can use our RESTful API server image. This container/server will run in the background.
To install YAKE! straight "on the metal" or you want to integrate it in your Python app, you can install it and its dependencies.
Option 1. YAKE as a CLI utility inside a Docker container
First, install Docker. Ubuntu users, please see our script below for a complete installation script.
Then, run:
 docker run liaad/yake:latest -ti "Caffeine is a central nervous system (CNS) stimulant of the methylxanthine class.[10] It is the world's most widely consumed psychoactive drug. Unlike many other psychoactive substances, it is legal and unregulated in nearly all parts of the world. There are several known mechanisms of action to explain the effects of caffeine. The most prominent is that it reversibly blocks the action of adenosine on its receptor and consequently prevents the onset of drowsiness induced by adenosine. Caffeine also stimulates certain portions of the autonomic nervous system." 
 Example text from Wikipedia 
Option 2. REST API Server in a Docker container
This install will provide you a mirror of the original REST API of YAKE! available here.
 docker run -p 5000:5000 -d liaad/yake-server:latest 
After it starts up, the container will run in the background, at http://127.0.0.1:5000. To access the YAKE! API documentation, go to http://127.0.0.1:5000/apidocs/.
You can test the RESTful API using curl:
 curl -X POST "http://localhost:5000/yake/" -H "accept: application/json" -H "Content-Type: application/json" \
-d @- &lt;&lt;'EOF'
{
 "language": "en",
 "max_ngram_size": 3,
 "number_of_keywords": 10,
 "text": "Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions. Details about the transaction remain somewhat vague , but given that Google is hosting its Cloud Next conference in San Francisco this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, was founded by Goldbloom and Ben Hamner in 2010. The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, it has managed to stay well ahead of them by focusing on its specific niche. The service is basically the de facto home for running data science and machine learning competitions. With Kaggle, Google is buying one of the largest and most active communities for data scientists ..."
}
EOF 
 Example text from Wikipedia 
Option 3. Standalone Installation (for development or integration)
Requirements
Python3
Installation
To install Yake using pip:
 pip install git+https://github.com/LIAAD/yake 
To upgrade using pip:
 pip install git+https://github.com/LIAAD/yake –-upgrade 
Usage (Command line)
How to use it on your favorite command line
 Usage: yake [OPTIONS]
Options:
	-ti, --text_input TEXT Input text, SURROUNDED by single quotes(')
	-i, --input_file TEXT Input file
	-l, --language TEXT Language
	-n, --ngram-size INTEGER Max size of the ngram.
	-df, --dedup-func [leve|jaro|seqm]
	Deduplication function.
	-dl, --dedup-lim FLOAT Deduplication limiar.
	-ws, --window-size INTEGER Window size.
	-t, --top INTEGER Number of keyphrases to extract
	-v, --verbose	Gets detailed information (such as the score)
	--help Show this message and exit. 
Usage (Python)
How to use it on Python
 import yake
text = "Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning "\
"competitions. Details about the transaction remain somewhat vague, but given that Google is hosting its Cloud "\
"Next conference in San Francisco this week, the official announcement could come as early as tomorrow. "\
"Reached by phone, Kaggle co-founder CEO Anthony Goldbloom declined to deny that the acquisition is happening. "\
"Google itself declined 'to comment on rumors'. Kaggle, which has about half a million data scientists on its platform, "\
"was founded by Goldbloom and Ben Hamner in 2010. "\
"The service got an early start and even though it has a few competitors like DrivenData, TopCoder and HackerRank, "\
"it has managed to stay well ahead of them by focusing on its specific niche. "\
"The service is basically the de facto home for running data science and machine learning competitions. "\
"With Kaggle, Google is buying one of the largest and most active communities for data scientists - and with that, "\
"it will get increased 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 " 
assuming default parameters
 kw_extractor = yake.KeywordExtractor()
keywords = kw_extractor.extract_keywords(text)
for kw in keywords:
	print(kw) 
specifying parameters
 language = "en"
max_ngram_size = 3
deduplication_threshold = 0.9
deduplication_algo = 'seqm'
windowSize = 1
numOfKeywords = 20
custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, dedupLim=deduplication_threshold, dedupFunc=deduplication_algo, windowsSize=windowSize, top=numOfKeywords, features=None)
keywords = custom_kw_extractor.extract_keywords(text)
for kw in keywords:
 print(kw) 
Output
The lower the score, the more relevant the keyword is.
 ('google', 0.026580863364597897)
('kaggle', 0.0289005976239829)
('ceo anthony goldbloom', 0.029946071606210194)
('san francisco', 0.048810837074825336)
('anthony goldbloom declined', 0.06176910090701819)
('google cloud platform', 0.06261974476422487)
('co-founder ceo anthony', 0.07357749587020043)
('acquiring kaggle', 0.08723571551039863)
('ceo anthony', 0.08915156857226395)
('anthony goldbloom', 0.09123482372372106)
('machine learning', 0.09147989238151344)
('kaggle co-founder ceo', 0.093805063905847)
('data', 0.097574333771058)
('google cloud', 0.10260128641464673)
('machine learning competitions', 0.10773000650607861)
('francisco this week', 0.11519915079240485)
('platform', 0.1183512305596321)
('conference in san', 0.12392066376108138)
('service', 0.12546743261462942)
('goldbloom', 0.14611408778815776) 
Highlighting Feature
Highlighting feature will tag every keyword in the text with the default tag &lt;kw&gt;.
 from yake.highlight import TextHighlighter
th = TextHighlighter(max_ngram_size = 3)
th.highlight(text, keywords) 
Output
By default, keywords will be highlighted using the tag 'kw'.
  Sources tell us that &lt;kw&gt;google&lt;/kw&gt; is &lt;kw&gt;acquiring kaggle&lt;/kw&gt;, a platform that &lt;kw&gt;hosts data science&lt;/kw&gt; and &lt;kw&gt;machine learning&lt;/kw&gt; competitions. Details about the transaction remain somewhat vague , but given that &lt;kw&gt;google&lt;/kw&gt; is hosting its Cloud Next conference in &lt;kw&gt;san francisco&lt;/kw&gt; this week, the official announcement could come as early as tomorrow. Reached by phone, Kaggle co-founder &lt;kw&gt;ceo anthony goldbloom&lt;/kw&gt; declined to deny that the acquisition is happening. &lt;kw&gt;google&lt;/kw&gt; itself declined 'to comment on rumors'.
.....
.....
  
Custom Highlighting Feature
Besides tagging a text with the default tag, users can also specify their own custom highlight. In the following text, the tag &lt;span class='my_class' &gt; makes use of an hyphotetical function my_class whose purpose would be to highlight in white colour or the relevant keywords.
Output
 from yake.highlight import TextHighlighter
th = TextHighlighter(max_ngram_size = 3, highlight_pre = "&lt;span class='my_class' &gt;", highlight_post= "&lt;/span&gt;")
th.highlight(text, keywords) 
  self.highlight_postSources tell us that &lt;span class='my_class' &gt;google&lt;/span&gt; is &lt;span class='my_class' &gt;acquiring kaggle&lt;/span&gt;, a platform that &lt;span class='my_class' &gt;hosts data science&lt;/span&gt; and &lt;span class='my_class' &gt;machine learning&lt;/span&gt; self.highlight_postcompetitions. Details about the transaction remain somewhat vague , but given that &lt;span class='my_class' &gt;google&lt;/span&gt; is hosting self.highlight_postits Cloud Next conference in &lt;span class='my_class' &gt;san francisco&lt;/span&gt; this week, the official announcement could come as early self.highlight_postas tomorrow. Reached by phone, Kaggle co-founder &lt;span class='my_class' &gt;ceo anthony goldbloom&lt;/span&gt; declined to deny that the self.highlight_postacquisition is happening. &lt;span class='my_class' &gt;google&lt;/span&gt; itself declined 'to comment on rumors'.
.....
.....
  
Languages others than English
While English (en) is the default language, users can use YAKE! to extract keywords from whatever language they want to by specifying the the corresponding language universal code. The below example shows how to extract keywords from a portuguese text.
 text = '''
"Conta-me Histórias." Xutos inspiram projeto premiado. A plataforma "Conta-me Histórias" foi distinguida com o Prémio Arquivo.pt, atribuído a trabalhos inovadores de investigação ou aplicação de recursos preservados da Web, através dos serviços de pesquisa e acesso disponibilizados publicamente pelo Arquivo.pt . Nesta plataforma em desenvolvimento, o utilizador pode pesquisar sobre qualquer tema e ainda executar alguns exemplos predefinidos. Como forma de garantir a pluralidade e diversidade de fontes de informação, esta são utilizadas 24 fontes de notícias eletrónicas, incluindo a TSF. Uma versão experimental (beta) do "Conta-me Histórias" está disponível aqui.
A plataforma foi desenvolvida por Ricardo Campos investigador do LIAAD do INESC TEC e docente do Instituto Politécnico de Tomar, Arian Pasquali e Vitor Mangaravite, também investigadores do LIAAD do INESC TEC, Alípio Jorge, coordenador do LIAAD do INESC TEC e docente na Faculdade de Ciências da Universidade do Porto, e Adam Jatwot docente da Universidade de Kyoto.
'''
custom_kw_extractor = yake.KeywordExtractor(lan="pt")
keywords = custom_kw_extractor.extract_keywords(text)
for kw in keywords:
 print(kw) 
Output
 ('conta-me histórias', 0.006225012963810038)
('liaad do inesc', 0.01899063587015275)
('inesc tec', 0.01995432290332246)
('conta-me', 0.04513273690417472)
('histórias', 0.04513273690417472)
('prémio arquivo.pt', 0.05749361520927859)
('liaad', 0.07738867367929901)
('inesc', 0.07738867367929901)
('tec', 0.08109398065524037)
('xutos inspiram projeto', 0.08720742489353424)
('inspiram projeto premiado', 0.08720742489353424)
('adam jatwot docente', 0.09407053486771558)
('arquivo.pt', 0.10261392141666957)
('alípio jorge', 0.12190479662535166)
('ciências da universidade', 0.12368384021490342)
('ricardo campos investigador', 0.12789997272332762)
('politécnico de tomar', 0.13323587141127738)
('arian pasquali', 0.13323587141127738)
('vitor mangaravite', 0.13323587141127738)
('preservados da web', 0.13596322680882506) 
Related projects
YAKE! Mobile APP
YAKE! is now available on Google Play 
 pke - python keyphrase extraction
 https://github.com/boudinfl/pke - pke is an open source python-based keyphrase extraction toolkit. It
provides an end-to-end keyphrase extraction pipeline in which each component can
be easily modified or extended to develop new models. pke also allows for
easy benchmarking of state-of-the-art keyphrase extraction models, and
ships with supervised models trained on the SemEval-2010 dataset (http://aclweb.org/anthology/S10-1004).
Credits to https://github.com/boudinfl 
 SparkNLP - State of the Art Natural Language Processing framework
 https://github.com/JohnSnowLabs/spark-nlp - SparkNLP from John Snow Labs is an open source framework with full Python, Scala, and Java Support. Check their documentation, demo and google colab. A video on how to use spark nlp with yake can also be found here: https://events.johnsnowlabs.com/john-snow-labs-nlu-become-a-data-science-superhero-with-one-line-of-python-code 
 General Index by Archive.org
 https://archive.org/details/GeneralIndex - A catalogue of 19 billions of YAKE keywords extracted from 107 million papers. An article about the General Index project can also be found in Nature.
 textacy - NLP, before and after spaCy
 https://github.com/chartbeat-labs/textacy - textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. among other features it supports keyword extration using YAKE.
Credits to https://github.com/chartbeat-labs 
 Annif - Tool for automated subject indexing and classification
 https://github.com/NatLibFi/Annif/ - Annif is a multi-algorithm automated subject indexing tool for libraries, archives and museums. This repository is used for developing a production version of the system, based on ideas from the initial prototype. Official website http://annif.org/.
 Portulan Clarin - Services and data for researchers, innovators, students and language professionals
 https://portulanclarin.net/workbench/liaad-yake/ - Portulan Clarin is a Research Infrastructure for the Science and Technology of Language, belonging to the Portuguese National Roadmap of Research Infrastructures of Strategic Relevance, and part of the international research infrastructure CLARIN ERIC. It includes a demo of YAKE! among many other language technologies. Official website https://portulanclarin.net/.
How to install Docker
Here is the "just copy and paste" installations script for Docker in Ubuntu. Enjoy.
 # Install dependencies
sudo apt-get update
sudo apt-get install \
 apt-transport-https \
 ca-certificates \
 curl \
 software-properties-common
# Add Docker repo
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
 "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
 $(lsb_release -cs) \
 stable"
sudo apt-get update
# Install Docker
sudo apt-get install -y docker-ce
# Start Docker Daemon
sudo service docker start
# Add yourself to the Docker user group, otherwise docker will complain that
# it does not know if the Docker Daemon is running
sudo usermod -aG docker ${USER}
# Install docker-compose
sudo curl -L "https://github.com/docker/compose/releases/download/1.23.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
source ~/.bashrc
docker-compose --version
echo "Done!" 
Credits to https://github.com/silvae86 for the Docker scripts.
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
GitHub - LIAAD/yake: Single-document unsupervised keyword extraction
https://github.com/LIAAD/yake
http://yake.inesctec.pt/
Yake!
2022-01-01
None
<div><div>
 <h2>
 YAKE-Guided LDA approach for automatic classification of construction safety reports
 </h2>
 <h3>
 Hrishikesh Gadekar, Nikhil Bugalia
 </h3>
 <br />
 <h4>
 In: 39th International Symposium on Automation and Robotics in Construction (ISARC
 2022). pp. 451-458, 2022
 </h4>
 <br />
 </div>||||I|||| Yake!

 * Used By
 * Team
 * Scientific Articles
 * Influential Citations
 * Media
 * Install Yake!
 * Demo

 Keyword Extraction

 Extract keywords from texts in different languages and domains

 Go!
 Watch Video Google Play


 GitHub Stars

 Used By



 Iâm a company. Can I use YAKE?


 MEET THE TEAM

 Scientific Articles

 Influential Citations

 YAKE-Guided LDA approach for automatic classification of construction safety reports

 Hrishikesh Gadekar, Nikhil Bugalia


 In: 39th International Symposium on Automation and Robotics in Construction (ISARC 2022). pp. 451-458, 2022


 This paper tests the applicability of YAKE! integrated with Guided Latent Dirichlet allocation to automatically classify safety observations in construction. The keywords obtained from the domain corpus using YAKE are seeded in GLDA to classify nearly 13,000 safety reports

 Highlights on media



 Read More News
 © Copyright Arsha . All Rights Reserved
 Designed by BootstrapMade
YAKE-Guided LDA approach for automatic classification of construction safety reports
 
 
 Hrishikesh Gadekar, Nikhil Bugalia
 
 
 
 In: 39th International Symposium on Automation and Robotics in Construction (ISARC
 2022). pp. 451-458, 2022
Yake!
http://yake.inesctec.pt/
https://liaad.github.io/yake/
Home
2019-09-01
None
<div><div> <p>Unsupervised Approach for Automatic Keyword Extraction using Text Features.</p> <p><a href="https://liaad.github.io/yake/docs/getting_started">Get started now</a> <a href="https://share.streamlit.io/arianpasquali/yake/demo/streamlit_app.py">View demo</a> <a href="https://github.com/LIAAD/yake">GitHub</a></p><hr/> <p>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.</p> <p><a href="https://colab.research.google.com/github/LIAAD/yake/blob/gh-pages/notebooks/YAKE_tutorial.ipynb"></a></p> <h2> <a href="#background"></a> Background </h2> <p>Extracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents. Yake! is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further 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.</p> <h2> <a href="#main-features"></a> Main Features </h2> <ul> <li>Unsupervised approach</li> <li>Corpus-Independent</li> <li>Domain and Language Independent</li> <li>Single-Document</li> </ul> <h2> <a href="#references"></a> References </h2> <p>If you use “YAKE” in a work that leads to a scientific publication, we would appreciate it if you would kindly cite it in your manuscript.</p> <ul> <li> <p>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. <a href="https://doi.org/10.1016/j.ins.2019.09.013">pdf</a></p> </li> <li> <p>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. <a href="https://link.springer.com/chapter/10.1007/978-3-319-76941-7_63">pdf</a> (ECIR’18 Best Short Paper)</p> </li> <li> <p>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. <a href="https://link.springer.com/chapter/10.1007/978-3-319-76941-7_80">pdf</a></p> </li> </ul> <h2> <a href="#license"></a> License </h2> <p>Copyright (C) 2018, INESC TEC <a href="https://github.com/LIAAD/yake/blob/master/LICENSE">license</a>.</p> <h2> <a href="#contributing"></a> Contributing </h2> <p>When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change. Read more about becoming a contributor in <a href="https://github.com/LIAAD/yake/blob/master/CONTRIBUTING.rst">our GitHub repo</a>.</p> <h3> <a href="#thank-you-to-the-contributors-of-yake"></a> Thank you to the contributors of YAKE! </h3> <ul> <li> <a href="https://github.com/arianpasquali"></a> </li> <li> <a href="https://github.com/rncampos"></a> </li> <li> <a href="https://github.com/silvae86"></a> </li> <li> <a href="https://github.com/JMendes1995"></a> </li> <li> <a href="https://github.com/erip"></a> </li> <li> <a href="https://github.com/vitordouzi"></a> </li> <li> <a href="https://github.com/andriyor"></a> </li> <li> <a href="https://github.com/readie-nf"></a> </li> <li> <a href="https://github.com/moty66"></a> </li> <li> <a href="https://github.com/patrickjae"></a> </li> <li> <a href="https://github.com/andefined"></a> </li> <li> <a href="https://github.com/martijnbroekman"></a> </li> </ul> <p>If you are feeling nostalgic you can access the old site <a href="http://yake.inesctec.pt/">here</a>.</p> <hr/> </div></div>||||I|||| yake
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 This site uses Just the Docs, a documentation theme for Jekyll.
 * Home
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 Yet Another Keyword Extractor (YAKE)

 Unsupervised Approach for Automatic Keyword Extraction using Text Features.

 Get started now View demo GitHub

 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.

 Background

 Extracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents. Yake! is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further 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.

 Main Features

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

 References

 If you use “YAKE” in a work that leads to a scientific publication, we would appreciate it if you would kindly cite it in your manuscript.

 * 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

 * 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 (ECIR’18 Best Short Paper)

 * 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

 License

 Copyright (C) 2018, INESC TEC license.

 Contributing

 When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change. Read more about becoming a contributor in our GitHub repo.

 Thank you to the contributors of YAKE!

 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 

 If you are feeling nostalgic you can access the old site here.

 Back to top

 Copyright © 2018-2022 INESC TEC. Distributed by an INESCTEC license.

Unsupervised Approach for Automatic Keyword Extraction using Text Features.  Get started now View demo GitHub   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.        Background  Extracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents. Yake! is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, Yake! does not rely on dictionaries nor thesauri, neither is trained against any corpora. Instead, it follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further 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.    Main Features   Unsupervised approach Corpus-Independent Domain and Language Independent Single-Document     References  If you use “YAKE” in a work that leads to a scientific publication, we would appreciate it if you would kindly cite it in your manuscript.   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    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 (ECIR’18 Best Short Paper)   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       License  Copyright (C) 2018, INESC TEC license.    Contributing  When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change. Read more about becoming a contributor in our GitHub repo.    Thank you to the contributors of YAKE!                                                    If you are feeling nostalgic you can access the old site here.
Home
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2023-11-29
None
<div><div><table><tbody><tr><th>Yake</th></tr><tr><th>Biographical information</th></tr><tr><th>Ethnicity</th>
<td>
Unknown</td></tr><tr><th>Nationality</th>
<td>
<a href="https://stormlightarchive.fandom.com/wiki/Alethkar#Alethi">Alethi</a></td></tr><tr><th>Gender</th>
<td>
Male</td></tr><tr><th>Status</th>
<td>
Alive</td></tr><tr><th>Occupation</th>
<td>
<a href="https://stormlightarchive.fandom.com/wiki/Bridgeman">Bridgeman</a>, <a href="https://stormlightarchive.fandom.com/wiki/Windrunners">Windrunner</a> <a href="https://stormlightarchive.fandom.com/wiki/Squires">Squire</a></td></tr><tr><th>Appears in</th>
<td>
<a href="https://stormlightarchive.fandom.com/wiki/The_Way_of_Kings">The Way of Kings</a>, <a href="https://stormlightarchive.fandom.com/wiki/Words_of_Radiance">Words of Radiance</a>, <a href="https://stormlightarchive.fandom.com/wiki/Oathbringer">Oathbringer</a></td></tr></tbody></table>
<p><b>Yake</b> is an Alethi bridgeman in <a href="https://stormlightarchive.fandom.com/wiki/Highprince">Highprince</a> <a href="https://stormlightarchive.fandom.com/wiki/Torol_Sadeas">Sadeas</a>'s army. He is a member of <a href="https://stormlightarchive.fandom.com/wiki/Bridge_Four">Bridge Four</a>. In a bridge run, he caught an arrow in his arm.<sup><a href="#cite_note-Rtwokc46-1">[1]</a></sup>
</p><p>Yake is one of three bridgemen (the others being <a href="https://stormlightarchive.fandom.com/wiki/Moash">Moash</a> and <a href="https://stormlightarchive.fandom.com/wiki/Teft">Teft</a>) who were the first brought to be introduced to fighting with a <a href="https://stormlightarchive.fandom.com/wiki/Shardblade">Shardblade</a> under the tutelage of <a href="https://stormlightarchive.fandom.com/wiki/Zahel">Zahel</a>. He contradict's Zahel's description of Shardblades as being "just a tool."<sup><a href="#cite_note-Rworc44-2">[2]</a></sup>
</p><p>When training a bulk of Windrunner squire hopefuls, Yake slaps <a href="https://stormlightarchive.fandom.com/wiki/Rlain">Rlain</a> on the back and tells him that should show them how a real bridgeman does a <a href="https://stormlightarchive.fandom.com/wiki/Kata">kata</a>.<sup><a href="#cite_note-Robc55-3">[3]</a></sup>
</p>
<h2></h2>
<p>He is described as a "stout man" when he first begins using the Shardblade.<sup><a href="#cite_note-Rworc44-2">[2]</a></sup>
</p>
<h2></h2>
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<div><table><tbody><tr><th><div><p><a href="https://stormlightarchive.fandom.com/wiki/Template:Navbox_characters"></a>··<a href="https://stormlightarchive.fandom.com/wiki/Template:Navbox_characters?action=edit"></a></p></div><p>
Characters</p></th></tr><tr><td></td></tr><tr><th></th><td><div>
<p><a href="https://stormlightarchive.fandom.com/wiki/Kaladin">Kaladin</a> · <a href="https://stormlightarchive.fandom.com/wiki/Shallan_Davar">Shallan</a> · <a href="https://stormlightarchive.fandom.com/wiki/Dalinar_Kholin">Dalinar</a> · <a href="https://stormlightarchive.fandom.com/wiki/Szeth">Szeth</a> · <a href="https://stormlightarchive.fandom.com/wiki/Eshonai">Eshonai</a> · <a href="https://stormlightarchive.fandom.com/wiki/Jasnah_Kholin">Jasnah</a> · <a href="https://stormlightarchive.fandom.com/wiki/Taravangian">Taravangian</a> · <a href="https://stormlightarchive.fandom.com/wiki/Adolin_Kholin">Adolin</a> · <a href="https://stormlightarchive.fandom.com/wiki/Talenel%27Elin">Talenel'Elin</a> · <a href="https://stormlightarchive.fandom.com/wiki/Hoid">Hoid</a></p></div></td></tr><tr><td></td></tr><tr><th>Other</th><td><div>
<p><a href="https://stormlightarchive.fandom.com/wiki/Sylphrena">Sylphrena</a> · <a href="https://stormlightarchive.fandom.com/wiki/Elhokar_Kholin">Elhokar</a> · <a href="https://stormlightarchive.fandom.com/wiki/Navani_Kholin">Navani</a> · <a href="https://stormlightarchive.fandom.com/wiki/Torol_Sadeas">Sadeas</a> · <a href="https://stormlightarchive.fandom.com/wiki/Renarin_Kholin">Renarin</a> · <a href="https://stormlightarchive.fandom.com/wiki/Teft">Teft</a> · <a href="https://stormlightarchive.fandom.com/wiki/Lift">Lift</a> · <a href="https://stormlightarchive.fandom.com/wiki/Rysn">Rysn</a> · <a href="https://stormlightarchive.fandom.com/wiki/Category:Characters">More characters</a></p></div></td></tr></tbody></table></div>
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 in: Gender: Male, Status: Alive, Characters,
 and 3 more
 * Alethkar (people)
 * Bridgemen
 * Military (people)

 Yake

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 Yake 
 Biographical information
 Ethnicity Unknown 
 Nationality Alethi 
 Gender Male 
 Status Alive 
 Occupation Bridgeman, Windrunner Squire 
 Appears in The Way of Kings, Words of Radiance, Oathbringer
 

 Yake is an Alethi bridgeman in Highprince Sadeas's army. He is a member of Bridge Four. In a bridge run, he caught an arrow in his arm.[1]

 Yake is one of three bridgemen (the others being Moash and Teft) who were the first brought to be introduced to fighting with a Shardblade under the tutelage of Zahel. He contradict's Zahel's description of Shardblades as being "just a tool."[2]

 When training a bulk of Windrunner squire hopefuls, Yake slaps Rlain on the back and tells him that should show them how a real bridgeman does a kata.[3]


 Appearance [ ]

 He is described as a "stout man" when he first begins using the Shardblade.[2]

 References [ ]

 1. ↑ The Way of Kings , 46. Child of Tanavast
 2. ↑ 2.0 2.1 Words of Radiance , 44. One Form Of Justice
 3. ↑ Oathbringer , 55. Alone Together
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Yake   Biographical information   Ethnicity
Unknown   Nationality
Alethi    Gender
Male   Status
Alive   Occupation
Bridgeman, Windrunner Squire    Appears in
The Way of Kings, Words of Radiance, Oathbringer    
 Yake is an Alethi bridgeman in Highprince Sadeas's army. He is a member of Bridge Four. In a bridge run, he caught an arrow in his arm. [1] 
 Yake is one of three bridgemen (the others being Moash and Teft) who were the first brought to be introduced to fighting with a Shardblade under the tutelage of Zahel. He contradict's Zahel's description of Shardblades as being "just a tool." [2] 
 When training a bulk of Windrunner squire hopefuls, Yake slaps Rlain on the back and tells him that should show them how a real bridgeman does a kata. [3] 
 
He is described as a "stout man" when he first begins using the Shardblade. [2] 
 
 
 
 
 
 
        ··    
Characters           
 Kaladin · Shallan · Dalinar · Szeth · Eshonai · Jasnah · Taravangian · Adolin · Talenel'Elin · Hoid          Other  
 Sylphrena · Elhokar · Navani · Sadeas · Renarin · Teft · Lift · Rysn · More characters
Yake
https://stormlightarchive.fandom.com/wiki/Yake
https://www.urbandictionary.com/define.php?term=yake
Urban Dictionary: yake
2006-06-13
None
<div><div><main><section><div><div><div><ul><li><a href="https://twitter.com/intent/tweet?text=http%3A%2F%2Fyake.urbanup.com%2F1806810"></a></li><li><a href="https://www.facebook.com/sharer/sharer.php?u=http%3A%2F%2Fyake.urbanup.com%2F1806810"></a></li></ul></div><div><p>a person who creates <a href="https://www.urbandictionary.com/define.php?term=horrible">horrible</a> <a href="https://www.urbandictionary.com/define.php?term=ideas">ideas</a> in <a href="https://www.urbandictionary.com/define.php?term=a%20time">a time</a> of need</p></div><div><p><a href="https://www.urbandictionary.com/define.php?term=Matt">Matt</a> is <a href="https://www.urbandictionary.com/define.php?term=the%20biggest">the biggest</a> yake on <a href="https://www.urbandictionary.com/define.php?term=planet%20Earth">planet Earth</a>!</p></div><p>by <a href="https://www.urbandictionary.com/author.php?author=erid">erid</a> June 13, 2006</p><div><a href="https://www.urbandictionary.com/remove.form.php?reconsider%5Bdefid_to_remove%5D=1806810"></a></div></div><p><a href="https://urbandictionary.store/products/mug?defid=1806810">Get the mug.</a></p></div><div><div><div><h2><a href="https://www.urbandictionary.com/define.php?term=yake">yake</a></h2><div><ul><li><a href="https://twitter.com/intent/tweet?text=http%3A%2F%2Fyake.urbanup.com%2F16161508"></a></li><li><a href="https://www.facebook.com/sharer/sharer.php?u=http%3A%2F%2Fyake.urbanup.com%2F16161508"></a></li></ul></div></div><div><p>when <a href="https://www.urbandictionary.com/define.php?term=someone">someone</a> <a href="https://www.urbandictionary.com/define.php?term=throws">throws</a> up</p></div><div><p><a href="https://www.urbandictionary.com/define.php?term=yo">yo</a> <a href="https://www.urbandictionary.com/define.php?term=kailey">kailey</a> <a href="https://www.urbandictionary.com/define.php?term=yaked">yaked</a>/ kailey is about yake</p></div><div><p>by <a href="https://www.urbandictionary.com/author.php?author=lord%20fardquad">lord fardquad</a> July 7, 2021</p></div><div><a href="https://www.urbandictionary.com/remove.form.php?reconsider%5Bdefid_to_remove%5D=16161508"></a></div></div><p><a href="https://urbandictionary.store/products/mug?defid=16161508">Get the mug.</a></p></div><div><div><div><h2><a href="https://www.urbandictionary.com/define.php?term=yake">yake</a></h2><div><ul><li><a href="https://twitter.com/intent/tweet?text=http%3A%2F%2Fyake.urbanup.com%2F11970735"></a></li><li><a href="https://www.facebook.com/sharer/sharer.php?u=http%3A%2F%2Fyake.urbanup.com%2F11970735"></a></li></ul></div></div><div><p>To <a href="https://www.urbandictionary.com/define.php?term=walk%20up">walk up</a> to someone and slowly <a href="https://www.urbandictionary.com/define.php?term=stroke">stroke</a> their <a href="https://www.urbandictionary.com/define.php?term=leg">leg</a>.</p></div><div><p>I <a href="https://www.urbandictionary.com/define.php?term=walked">walked</a> up to john and <a href="https://www.urbandictionary.com/define.php?term=yaked">yaked</a> his <a href="https://www.urbandictionary.com/define.php?term=leg">leg</a>.</p></div><div><p>by <a href="https://www.urbandictionary.com/author.php?author=Kfjushahwbdbc">Kfjushahwbdbc</a> September 18, 2017</p></div><div><a href="https://www.urbandictionary.com/remove.form.php?reconsider%5Bdefid_to_remove%5D=11970735"></a></div></div><p><a href="https://urbandictionary.store/products/mug?defid=11970735">Get the mug.</a></p></div><div><div><div><h2><a href="https://www.urbandictionary.com/define.php?term=yake">yake</a></h2><div><ul><li><a href="https://twitter.com/intent/tweet?text=http%3A%2F%2Fyake.urbanup.com%2F8297902"></a></li><li><a href="https://www.facebook.com/sharer/sharer.php?u=http%3A%2F%2Fyake.urbanup.com%2F8297902"></a></li></ul></div></div><div><p><a href="https://www.urbandictionary.com/define.php?term=Someone">Someone</a> that is <a href="https://www.urbandictionary.com/define.php?term=wrong">wrong</a> or <a href="https://www.urbandictionary.com/define.php?term=unusual">unusual</a></p></div><div><p><a href="https://www.urbandictionary.com/define.php?term=James">James</a> is so yake, He <a href="https://www.urbandictionary.com/define.php?term=thinks">thinks</a> he's <a href="https://www.urbandictionary.com/define.php?term=the%20best%20at%20everything">the best at everything</a>!</p></div><div><p>by <a href="https://www.urbandictionary.com/author.php?author=Liebby">Liebby</a> May 31, 2015</p></div><div><a href="https://www.urbandictionary.com/remove.form.php?reconsider%5Bdefid_to_remove%5D=8297902"></a></div></div><p><a href="https://urbandictionary.store/products/mug?defid=8297902">Get the mug.</a></p></div><div><div><div><h2><a href="https://www.urbandictionary.com/define.php?term=yaked">yaked</a></h2><div><ul><li><a href="https://twitter.com/intent/tweet?text=http%3A%2F%2Fyaked.urbanup.com%2F2721407"></a></li><li><a href="https://www.facebook.com/sharer/sharer.php?u=http%3A%2F%2Fyaked.urbanup.com%2F2721407"></a></li></ul></div></div><div><p>to be completely <a href="https://www.urbandictionary.com/define.php?term=coked%20out">coked out</a>. doing to <a href="https://www.urbandictionary.com/define.php?term=much">much</a> <a href="https://www.urbandictionary.com/define.php?term=cocaine">cocaine</a>.</p></div><div><p>Dude, <a href="https://www.urbandictionary.com/define.php?term=last%20night">last night</a> I was so yaked out <a href="https://www.urbandictionary.com/define.php?term=at%20that">at that</a> <a href="https://www.urbandictionary.com/define.php?term=show">show</a>!</p></div><div><p>by <a href="https://www.urbandictionary.com/author.php?author=BlaZdOut">BlaZdOut</a> December 2, 2007</p></div><div><a href="https://www.urbandictionary.com/remove.form.php?reconsider%5Bdefid_to_remove%5D=2721407"></a></div></div><p><a href="https://urbandictionary.store/products/mug?defid=2721407">Get the mug.</a></p></div><div><div><div><h2><a href="https://www.urbandictionary.com/define.php?term=YAKE">YAKE</a></h2><div><ul><li><a href="https://twitter.com/intent/tweet?text=http%3A%2F%2Fyake.urbanup.com%2F15649649"></a></li><li><a href="https://www.facebook.com/sharer/sharer.php?u=http%3A%2F%2Fyake.urbanup.com%2F15649649"></a></li></ul></div></div><div><p>It’s a <a href="https://www.urbandictionary.com/define.php?term=greeting">greeting</a> ~ <a href="https://www.urbandictionary.com/define.php?term=peace">peace</a>, <a href="https://www.urbandictionary.com/define.php?term=hello">hello</a>, hey, hi</p></div><div><p><a href="https://www.urbandictionary.com/define.php?term=Yake">Yake</a> ✌️ How you doing?</p><p>Yake and making the ✌️Sign - means Hey or <a href="https://www.urbandictionary.com/define.php?term=Peace%20in">Peace in</a> <a href="https://www.urbandictionary.com/define.php?term=New%20Haven">New Haven</a>, Ky</p></div><div><p>by <a href="https://www.urbandictionary.com/author.php?author=Emmaleigh%20Wimson">Emmaleigh Wimson</a> January 11, 2021</p></div><div><a href="https://www.urbandictionary.com/remove.form.php?reconsider%5Bdefid_to_remove%5D=15649649"></a></div></div><p><a href="https://urbandictionary.store/products/mug?defid=15649649">Get the mug.</a></p></div><div><div><div><h2><a href="https://www.urbandictionary.com/define.php?term=yaking">yaking</a></h2><div><ul><li><a href="https://twitter.com/intent/tweet?text=http%3A%2F%2Fyaking.urbanup.com%2F3273942"></a></li><li><a href="https://www.facebook.com/sharer/sharer.php?u=http%3A%2F%2Fyaking.urbanup.com%2F3273942"></a></li></ul></div></div><div><p><a href="https://www.urbandictionary.com/define.php?term=Incessant">Incessant</a> <a href="https://www.urbandictionary.com/define.php?term=talking">talking</a> that seems to go and on without any <a href="https://www.urbandictionary.com/define.php?term=end">end</a>.</p></div><div><p><a href="https://www.urbandictionary.com/define.php?term=We%20would">We would</a> have gotten some <a href="https://www.urbandictionary.com/define.php?term=work%20done">work done</a> if you guys wouldn't have spent the last <a href="https://www.urbandictionary.com/define.php?term=two%20hours">two hours</a> yaking.</p></div><div><p>by <a href="https://www.urbandictionary.com/author.php?author=Optimist%20with%20experience">Optimist with experience</a> August 10, 2008</p></div><div><a href="https://www.urbandictionary.com/remove.form.php?reconsider%5Bdefid_to_remove%5D=3273942"></a></div></div><p><a href="https://urbandictionary.store/products/mug?defid=3273942">Get the mug.</a></p></div><div><ul><li></li><li><a 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 * 

 yake

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 a person who creates horrible ideas in a time of need
 Matt is the biggest yake on planet Earth!
 by erid June 13, 2006
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 yake

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 * 
 when someone throws up
 yo kailey yaked/ kailey is about yake
 by lord fardquad July 7, 2021
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 yake

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 * 
 To walk up to someone and slowly stroke their leg.
 I walked up to john and yaked his leg.
 by Kfjushahwbdbc September 18, 2017
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 yake

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 * 
 Someone that is wrong or unusual
 James is so yake, He thinks he's the best at everything!
 by Liebby May 31, 2015
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 yaked

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 * 
 to be completely coked out. doing to much cocaine.
 Dude, last night I was so yaked out at that show!
 by BlaZdOut December 2, 2007
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 YAKE

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 * 
 It’s a greeting ~ peace, hello, hey, hi
 Yake ✌️ How you doing?

 Yake and making the ✌️Sign - means Hey or Peace in New Haven, Ky
 by Emmaleigh Wimson January 11, 2021
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 yaking

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 * 
 Incessant talking that seems to go and on without any end.
 We would have gotten some work done if you guys wouldn't have spent the last two hours yaking.
 by Optimist with experience August 10, 2008
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a person who creates horrible ideas in a time of need    Matt is the biggest yake on planet Earth!  by erid June 13, 2006       Get the mug.       yake                when someone throws up    yo kailey yaked/ kailey is about yake   by lord fardquad July 7, 2021        Get the mug.       yake                To walk up to someone and slowly stroke their leg.   I walked up to john and yaked his leg.   by Kfjushahwbdbc September 18, 2017        Get the mug.       yake                 Someone that is wrong or unusual     James is so yake, He thinks he's the best at everything!   by Liebby May 31, 2015        Get the mug.       yaked                to be completely coked out. doing to much cocaine.   Dude, last night I was so yaked out at that show!   by BlaZdOut December 2, 2007        Get the mug.       YAKE                It’s a greeting ~ peace, hello, hey, hi    Yake ✌️ How you doing? Yake and making the ✌️Sign - means Hey or Peace in New Haven, Ky   by Emmaleigh Wimson January 11, 2021        Get the mug.       yaking                 Incessant talking that seems to go and on without any end.    We would have gotten some work done if you guys wouldn't have spent the last two hours yaking.   by Optimist with experience August 10, 2008        Get the mug.        2   3     Next ›   Last »      More random definitions
Urban Dictionary: yake
https://www.urbandictionary.com/define.php?term=yake
https://www.sebrightproducts.com/equipment/xtractor/
Xtractor Beverage Destruction Solution - Sebright Products Inc
2023-06-14T03:08:32Z

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	<h4>The Xtractor is Your Complete Full Beverage Destruction and Recycling Solution!</h4>
<p>Sebright Products/Bright Technologies 23 years of experience in liquids/solids separation technology was used in the design of the Xtractor and optional components to insure a clean and robust design. Above all, the stainless steel construction of this equipment helps insure a long service life and appealing appearance when dealing with low pH liquids.</p>
<p>Designed to not only remove the liquid content from the package, the Xtractor is also designed to contain the liquid and crushes the packaging in a clean and controlled operation. In fact, the Xtractor and its accessories are designed to operate as a sealed unit and keep the liquid in the equipment effluent sumps and off from your floor.</p>
<p>Whether your interest is in repurposing the liquid or recycling of the package or both, the Xtractor is the right choice for moderate volumes of liquid products in containers ranging from 0.5 up to 4 liters. In fact, the Xtractor is able to achieve weight reduction of up to 93% and volume reduction of up to 90%, and process up to 10 cubic yards (7.5 M3) per hour of material1.</p>
<p>Additionally, the Xtractor can handle multiple packaging materials in the same machine. Change over from one package type to another merely requires verifying that all of the previous product has been cleared from the feed equipment and Xtractor and making sure the two products are separated at the discharge end and go into the appropriate receiving containers.</p>
<p>A number of discharge options available to meet your needs. It can discharge directly into a tote, cart or an optional discharge chute or conveyor may be used to put the material directly into a compactor, open top container or baler.</p>
<h4>Optional Perforator</h4>
<p>An optional container Perforator is recommended for plastic packaging applications to insure maximum liquid recovery. The dual perforating rollers can handle most plastic beverage containers from 0.5 – 4 liter sizes. The perforator does not shred containers so both aluminum cans and plastic containers can still be processed in one system. The Perforator features two direct drive gear motors as well as a stainless steel frame and perforating roller construction. Two sealed polycarbonate windows allow convenient monitoring of the perforator operation.</p>
<h4>Feed Conveyors</h4>
<p>Our optional feed conveyors take the guess work on how to load your material into the Xtractor. First of all, the conveyors feature a stainless steel frame, hopper and 40 gallon liquid collection pan, as well as, a dependable direct drive gear motor. Two standard loading hoppers allow for selection of 11 different equipment layout options. The spacious 1.4 cubic yard (1.0 M3) loading hopper can be loaded manually or with an optional hydraulic cart dumper. Most importantly, all system equipment is integrated into the Xtractor controls for seamless, automatic operation that needs minimum operator attention.</p>
<p>Volumes of materials processed vary by container size and type. Aluminum cans process faster than PET containers and larger containers process faster than small ones etc.</p><ul>
</ul>	</div></div></body></html>
The Xtractor is Your Complete Full Beverage Destruction and Recycling Solution!
Sebright Products/Bright Technologies 23 years of experience in liquids/solids separation technology was used in the design of the Xtractor and optional components to insure a clean and robust design. Above all, the stainless steel construction of this equipment helps insure a long service life and appealing appearance when dealing with low pH liquids.
Designed to not only remove the liquid content from the package, the Xtractor is also designed to contain the liquid and crushes the packaging in a clean and controlled operation. In fact, the Xtractor and its accessories are designed to operate as a sealed unit and keep the liquid in the equipment effluent sumps and off from your floor.
Whether your interest is in repurposing the liquid or recycling of the package or both, the Xtractor is the right choice for moderate volumes of liquid products in containers ranging from 0.5 up to 4 liters. In fact, the Xtractor is able to achieve weight reduction of up to 93% and volume reduction of up to 90%, and process up to 10 cubic yards (7.5 M3) per hour of material1.
Additionally, the Xtractor can handle multiple packaging materials in the same machine. Change over from one package type to another merely requires verifying that all of the previous product has been cleared from the feed equipment and Xtractor and making sure the two products are separated at the discharge end and go into the appropriate receiving containers.
A number of discharge options available to meet your needs. It can discharge directly into a tote, cart or an optional discharge chute or conveyor may be used to put the material directly into a compactor, open top container or baler.
Optional Perforator
An optional container Perforator is recommended for plastic packaging applications to insure maximum liquid recovery. The dual perforating rollers can handle most plastic beverage containers from 0.5 – 4 liter sizes. The perforator does not shred containers so both aluminum cans and plastic containers can still be processed in one system. The Perforator features two direct drive gear motors as well as a stainless steel frame and perforating roller construction. Two sealed polycarbonate windows allow convenient monitoring of the perforator operation.
Feed Conveyors
Our optional feed conveyors take the guess work on how to load your material into the Xtractor. First of all, the conveyors feature a stainless steel frame, hopper and 40 gallon liquid collection pan, as well as, a dependable direct drive gear motor. Two standard loading hoppers allow for selection of 11 different equipment layout options. The spacious 1.4 cubic yard (1.0 M3) loading hopper can be loaded manually or with an optional hydraulic cart dumper. Most importantly, all system equipment is integrated into the Xtractor controls for seamless, automatic operation that needs minimum operator attention.
Volumes of materials processed vary by container size and type. Aluminum cans process faster than PET containers and larger containers process faster than small ones etc.
Xtractor Beverage Destruction Solution - Sebright Products Inc
https://www.sebrightproducts.com/equipment_category/xtractor/