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

Category	Features
Structural	Punctuation, sentence length and position.
Indicators	Claim-premise and first-person indicators.
Syntactic	Depth of constituency parse trees, presence of modal verbs, number of grammatical productions in the parse tree.
Embeddings	GloVe sentence embeddings (arithmetic mean of its word vectors).

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.