Argument Graph Mining

Code for the paper "Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs" presented at COMMA 2020.

System Requirements

Docker or uv

Installation

• Change any settings by overriding their values in a file called settings.local.toml.
• 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 --rm --service-ports {entrypoint}

Using uv, start the program with:

uv run argmining-{entrypoint}

The following entry points are available:

• app: Starts a flask server providing a website to perform interactive mining. The address is printed in the terminal.
• grpc: Starts a gRPC server to perform mining via API using the arg-services library.
• eval: 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:

uv run -m argmining.adu.training.train_adu

or

uv run -m argmining.adu.training.train_clpr

for the ADU or Claim/Premise classifier respectively.

Relationship Type

Start the jupyter notebook argmining/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.