ReCAP Argument Graph Mining

This program has been used to perform the evaluation of our proposed argument mining pipeline.

System Requirements

• Docker and Docker-Compose
• Alternatively: Python 3.7 and Poetry 1.0

Installation

• Duplicate the file config-example.yml to config.yml and adapt the settings to your liking.
• Create the folders data/input and data/output.
• If using Docker, please do not edit the web server settings.

Pipeline Usage

Docker will download all required data on the first run and thus may take a while. Future runs are cached and the app available immediately.

Using Docker, start the program with:

docker-compose run app python -m recap_am.{entrypoint}

Using Poetry, start the program with:

poetry run python -m recap_am.{entrypoint}

The following entry points are available:

• server: Starts a flask server providing a website to perform interactive mining. The address is printed in the terminal.
• cli: Start the pipeline without interaction.
• evaluate: Perform a grid computation with the parameters major claim method, relationship type threshold and graph construction method.

Per default, the program will look for input data in data/input. If you just want to convert plain text to argument graph, a .txt file is enough. If you want to compare a benchmark graph to the generated on, please provide a .json file conforming to the OVA-format.

Linguistic Features

Training the Classifiers

ADU and Claim/Premise

To start training, run the program with:

poetry run python -m recap_am.adu.training.train_adu

or

poetry run python -m recap_am.adu.training.train_clpr

for the ADU or Claim/Premise classifier respectively.

Relationship Type

Start the jupyter notebook recap_am/preprocessing/pipeline.ipynb within the container:

• Run cells & import libraries.
• Load your CSV data with the rows child, parent, stance into a DataFrame df.
• Run the following call to generate a dataset using GloVe Embeddings for either "english" or "german": data = prep_dataset(df, model = "glove", language = "english").
• Use data to train any classifier.