Provides scripts and code for executing some text mining workflows within a docker container.
Step 1: Clone omtd-simple-workflows by typing
git clone <repoURL>
Step 2: CD to omtd-simple-workflows directory that has been created. For building the projects type
./installProjectsDepedencies.sh
mvn clean install
Step 3: Create a docker image (named omtd-simple-workflows-docker) that contains everything that is needed.
./omtd-simple-workflows-createDockerImg.sh
Step 4: Use the following commands to a) create a container from the image produced by the previous step b) start the container c) get a bash shell inside the container.
sudo docker create --name omtd-simple-workflows -t omtd-simple-workflows-docker
sudo docker start omtd-simple-workflows
sudo docker exec -i -t omtd-simple-workflows /bin/bash
Inside the container try the following examples. The same examples can run and in the host machine.
Example 1:
# PDF2XMI example with DKPRo PdfReader. Reads a folder (../testInput) with PDFs and creates an output folder (../testOutput)
# with the respective XMIs that were produced.
./Linux_runDKPro_PDF2XMI_example.sh
# Check that the produced output is correct.
./checkDiff.sh ../testOutput/ ../testOutputPDFToXMIRef/
Example 2:
# Chebi example from UNIMAN. Reads a folder (../testInput3) with PDFs and creates an output folder (../testOutput2)
# with the respective XMIs that were produced
./Linux_runUNIMAN_Chebi_example.sh
Example 3:
# Topic Inference with DKPro (Mallet LDA). Reads a folder (../testInput) with PDFs and creates an output folder (../testOutput3)
# with the respective XMIs that were produced
./Linux_runDKPro_PDFLDAInference_example.sh
Example 4:
# Named Entity Resolution with DKPro (Stanford NER mode) for Social Science and Humanities. Reads a folder (../testInput2) with PDFs and creates an output folder (../testOutput4)
# with the respective XMIs that were produced
./Linux_runDKPro_PDFNERInference_example.sh
NOTES: All scripts are available at omtd-simple-workflows/scripts directory.