yake-dockerfile

Dockerfile for building an image of the LIAAD/yake Keyword Extration Library as a REST API.

It mirrors the original application available here.

Original credits go to the authors below!!

We did not build YAKE! but only packaged it into a Docker image. A copy of the original README from the LIAAD GitHub repository is shown below. See the original library repository at: https://github.com/LIAAD/yake

Running Yake via Docker:

docker run -d feupinfolab/yake-rest:latest

Testing

curl "http://127.0.0.1:7890/yake/v2/extract_keywords?max_ngram_size=3&number_of_keywords=30" \
  --header "Content-Type": "application/x-www-form-urlencoded" \
  --header "Accept: "application/json" \
  --request POST \
  --data '{\"content\":\"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.\"}'"

(Text from wikipedia)

Yet Another Keyword Extractor (Yake)

Unsupervised Approach for Automatic Keyword Extraction using Text Features

Documentation: https://pypi.python.org/pypi/yake.

Main Features

Unsupervised approach
Multi-Language Support
Single document

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.

Please cite the following works when using YAKE

Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., & Jatowt, A. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents Proceedings of the 40th European Conference on Information Retrieval (ECIR'18), Grenoble, France. March 26 – 29.

Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., & Jatowt, A. (2018). YAKE! Collection-independent Automatic Keyword Extractor Proceedings of the 40th European Conference on Information Retrieval (ECIR'18), Grenoble, France. March 26 – 29

feup-infolab / yake-rest-dockerfile