KEP package (http://github.com/liaad/kep) here available as a docker image, enables one to extract keyphrases from documents (single or multiple documents) by applying a number of algorithms, the big majority of which provided by pke an open-source package. Differently from PKE, we provide a ready to run code to extract keyphrases not only from a single document, but also in batch mode (i.e., several documents). More to the point, we consider 20 state-of-the-art datasets from which keyphrases may be extracted, and the corresponding dfs, lda and KEA pre-computed models (which contrasts with pke as only semeval-2010 models are made available).
KEP can extract keyphrases from 20 datasets:
Docker for Windows requires 64bit Windows 10 Pro with Hyper-V available.
If you have this, then proceed to download here: (https://docs.docker.com/docker-for-windows/install/#download-docker-for-windows) and click on Get Docker for Windows (Stable)
If your system does not meet the requirements to run Docker for Windows (e.g., 64bit Windows 10 Home), you can install Docker Toolbox, which uses Oracle Virtual Box instead of Hyper-V. In that case proceed to download here: (https://docs.docker.com/toolbox/overview/#ready-to-get-started) and click on Get Docker Toolbox for Windows
Docker for Mac will launch only if all of these requirements (https://docs.docker.com/docker-for-mac/install/#what-to-know-before-you-install) are met.
If you have this, then proceed to download here: (https://docs.docker.com/docker-for-mac/install/#download-docker-for-mac) and click on Get Docker for Mac (Stable)
If your system does not meet the requirements to run Docker for Mac, you can install Docker Toolbox, which uses Oracle Virtual Box instead of Hyper-V. In that case proceed to download here: (https://docs.docker.com/toolbox/overview/#ready-to-get-started) and click on Get Docker Toolbox for Mac
Proceed to download here: (https://docs.docker.com/engine/installation/#server)
Execute the following command on your docker machine:
docker pull liaad/kep
On your docker machine run the following to launch the image:
docker run -p 9999:8888 --user root liaad/kep
Then go to your browser and type in the following url:
http://<DOCKER-MACHINE-IP>:9999
where the IP may be the localhost or 192.168.99.100 if you are using a Docker Machine VM.
You will be required a token which you can find on your docker machine prompt. It will be something similar to this: http://eac214218126:8888/?token=ce459c2f581a5f56b90256aaa52a96e7e4b1705113a657e8. Copy paste the token (in this example, that would be: ce459c2f581a5f56b90256aaa52a96e7e4b1705113a657e8) to the browser, and voilá, you will have KEP package ready to run. Keep this token (for future references) or define a password.
Once you logged in, proceed by running the notebook that we have prepared for you.
Once you are done go to File - Shutdown.
If later on you decide to play with the same container, you should proceed as follows. The first thing to do is to get the container id:
docker ps -a
Next run the following commands:
docker start ContainerId
docker attach ContainerId (attach to a running container)
Nothing happens in your docker machine, but you are now ready open your browser as you did before:
http://<DOCKER-MACHINE-IP>:9999
Hopefully, you have saved the token or defined a password. If that is not the case, then you should run the following command (before doing start/attach) to have access to your token:
docker exec -it <docker_container_name> jupyter notebook list
Content type
Image
Digest
Size
1.4 GB
Last updated
over 6 years ago
docker pull liaad/kep